






from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers.models.gptj.modeling_tf_gptj import (
	    TFGPTJForCausalLM,
	    TFGPTJForQuestionAnswering,
	    TFGPTJForSequenceClassification,
	    TFGPTJModel,
	    shape_list,
	)


class        lowercase__       :

	def __init__(							self						,      __UpperCAmelCase    )->   Tuple:







							'''simple docstring'''





							lowerCAmelCase__	 =  parent
							lowerCAmelCase__	 =  13
							lowerCAmelCase__	 =  7
							lowerCAmelCase__	 =  True
							lowerCAmelCase__	 =  True
							lowerCAmelCase__	 =  True
							lowerCAmelCase__	 =  True
							lowerCAmelCase__	 =  True
							lowerCAmelCase__	 =  99
							lowerCAmelCase__	 =  32
							lowerCAmelCase__	 =  4
							lowerCAmelCase__	 =  2
							lowerCAmelCase__	 =  4
							lowerCAmelCase__	 =  37
							lowerCAmelCase__	 =  "gelu"
							lowerCAmelCase__	 =  0.1
							lowerCAmelCase__	 =  0.1
							lowerCAmelCase__	 =  512
							lowerCAmelCase__	 =  16
							lowerCAmelCase__	 =  2
							lowerCAmelCase__	 =  0.02
							lowerCAmelCase__	 =  3
							lowerCAmelCase__	 =  4
							lowerCAmelCase__	 =  None
							lowerCAmelCase__	 =  self.vocab_size - 1
							lowerCAmelCase__	 =  self.vocab_size - 1
							lowerCAmelCase__	 =  self.vocab_size - 1

	def 						UpperCAmelCase					(							self    )->   List[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size    )

							lowerCAmelCase__	 =  None
							if self.use_input_mask:
													lowerCAmelCase__	 =  random_attention_mask([self.batch_size, self.seq_length]    )

							lowerCAmelCase__	 =  None
							if self.use_token_type_ids:
													lowerCAmelCase__	 =  ids_tensor([self.batch_size, self.seq_length]						,      self.type_vocab_size    )

							lowerCAmelCase__	 =  None
							if self.use_mc_token_ids:
													lowerCAmelCase__	 =  ids_tensor([self.batch_size, self.num_choices]						,      self.seq_length    )

							lowerCAmelCase__	 =  None
							lowerCAmelCase__	 =  None
							lowerCAmelCase__	 =  None
							if self.use_labels:
													lowerCAmelCase__	 =  ids_tensor([self.batch_size]						,      self.type_sequence_label_size    )
													lowerCAmelCase__	 =  ids_tensor([self.batch_size, self.seq_length]						,      self.num_labels    )
													lowerCAmelCase__	 =  ids_tensor([self.batch_size]						,      self.num_choices    )

							lowerCAmelCase__	 =  GPTJConfig(
							    vocab_size=self.vocab_size						,      n_embd=self.hidden_size						,      n_layer=self.num_hidden_layers						,      n_head=self.num_attention_heads						,      intermediate_size=self.intermediate_size						,      hidden_act=self.hidden_act						,      hidden_dropout_prob=self.hidden_dropout_prob						,      attention_probs_dropout_prob=self.attention_probs_dropout_prob						,      n_positions=self.max_position_embeddings						,      type_vocab_size=self.type_vocab_size						,      initializer_range=self.initializer_range						,      bos_token_id=self.bos_token_id						,      eos_token_id=self.eos_token_id						,      pad_token_id=self.pad_token_id						,      rotary_dim=self.rotary_dim						,      return_dict=__UpperCAmelCase						,      )

							lowerCAmelCase__	 =  ids_tensor([self.num_hidden_layers, self.num_attention_heads]						,      2    )

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							    token_type_ids,
							    mc_token_ids,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							)

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      *__UpperCAmelCase    )->   Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJModel(config=__UpperCAmelCase    )
							lowerCAmelCase__	 =  {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							lowerCAmelCase__	 =  model(__UpperCAmelCase    )

							lowerCAmelCase__	 =  [input_ids, None, input_mask]  # None is the input for 'past'
							lowerCAmelCase__	 =  model(__UpperCAmelCase    )

							lowerCAmelCase__	 =  model(__UpperCAmelCase    )

							self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, self.seq_length, self.hidden_size)    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      *__UpperCAmelCase    )->   Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJModel(config=__UpperCAmelCase    )

							# first forward pass
							lowerCAmelCase__	 =  model(__UpperCAmelCase						,      token_type_ids=__UpperCAmelCase						,      use_cache=__UpperCAmelCase    )
							lowerCAmelCase__	 =  model(__UpperCAmelCase						,      token_type_ids=__UpperCAmelCase    )
							lowerCAmelCase__	 =  model(__UpperCAmelCase						,      token_type_ids=__UpperCAmelCase						,      use_cache=__UpperCAmelCase    )

							self.parent.assertTrue(len(__UpperCAmelCase    ) == len(__UpperCAmelCase    )    )
							self.parent.assertTrue(len(__UpperCAmelCase    ) == len(__UpperCAmelCase    ) + 1    )

							lowerCAmelCase__ ,							lowerCAmelCase__	 =  outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							lowerCAmelCase__	 =  ids_tensor((self.batch_size, 1)						,      config.vocab_size    )
							lowerCAmelCase__	 =  ids_tensor([self.batch_size, 1]						,      self.type_vocab_size    )

							# append to next input_ids and token_type_ids
							lowerCAmelCase__	 =  tf.concat([input_ids, next_tokens]						,      axis=-1    )
							lowerCAmelCase__	 =  tf.concat([token_type_ids, next_token_types]						,      axis=-1    )

							lowerCAmelCase__	 =  model(__UpperCAmelCase						,      token_type_ids=__UpperCAmelCase    )["last_hidden_state"]
							lowerCAmelCase__	 =  model(__UpperCAmelCase						,      token_type_ids=__UpperCAmelCase						,      past_key_values=__UpperCAmelCase    )[
							    "last_hidden_state"
							]

							# select random slice
							lowerCAmelCase__	 =  int(ids_tensor((1,)						,      shape_list(__UpperCAmelCase    )[-1]    )    )
							lowerCAmelCase__	 =  output_from_no_past[:, -1, random_slice_idx]
							lowerCAmelCase__	 =  output_from_past[:, 0, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(__UpperCAmelCase						,      __UpperCAmelCase						,      rtol=1E-6    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      *__UpperCAmelCase    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJModel(config=__UpperCAmelCase    )

							# create attention mask
							lowerCAmelCase__	 =  self.seq_length // 2
							lowerCAmelCase__	 =  tf.ones((self.batch_size, half_seq_length)						,      dtype=tf.intaa    )
							lowerCAmelCase__	 =  tf.zeros((self.batch_size, self.seq_length - half_seq_length)						,      dtype=tf.intaa    )
							lowerCAmelCase__	 =  tf.concat([attn_mask_begin, attn_mask_end]						,      axis=1    )

							# first forward pass
							lowerCAmelCase__ ,							lowerCAmelCase__	 =  model(__UpperCAmelCase						,      attention_mask=__UpperCAmelCase    ).to_tuple()

							# create hypothetical next token and extent to next_input_ids
							lowerCAmelCase__	 =  ids_tensor((self.batch_size, 1)						,      config.vocab_size    )

							# change a random masked slice from input_ids
							lowerCAmelCase__	 =  ids_tensor((1,)						,      __UpperCAmelCase    ).numpy() + 1
							lowerCAmelCase__	 =  ids_tensor((self.batch_size, self.seq_length)						,      config.vocab_size    )
							lowerCAmelCase__	 =  tf.range(self.seq_length    ) == (self.seq_length - random_seq_idx_to_change)
							lowerCAmelCase__	 =  tf.transpose(
							    tf.broadcast_to(tf.expand_dims(__UpperCAmelCase						,      -1    )						,      (self.seq_length, self.batch_size)    )    )
							lowerCAmelCase__	 =  tf.where(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )

							# append to next input_ids and attn_mask
							lowerCAmelCase__	 =  tf.concat([input_ids, next_tokens]						,      axis=-1    )
							lowerCAmelCase__	 =  tf.concat([attn_mask, tf.ones((shape_list(__UpperCAmelCase    )[0], 1)						,      dtype=tf.intaa    )]						,      axis=1    )

							# get two different outputs
							lowerCAmelCase__	 =  model(__UpperCAmelCase						,      attention_mask=__UpperCAmelCase    )["last_hidden_state"]
							lowerCAmelCase__	 =  model(__UpperCAmelCase						,      past_key_values=__UpperCAmelCase						,      attention_mask=__UpperCAmelCase    )[
							    "last_hidden_state"
							]

							# select random slice
							lowerCAmelCase__	 =  int(ids_tensor((1,)						,      shape_list(__UpperCAmelCase    )[-1]    )    )
							lowerCAmelCase__	 =  output_from_no_past[:, -1, random_slice_idx]
							lowerCAmelCase__	 =  output_from_past[:, 0, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(__UpperCAmelCase						,      __UpperCAmelCase						,      rtol=1E-1_2    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      *__UpperCAmelCase    )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJModel(config=__UpperCAmelCase    )

							lowerCAmelCase__	 =  input_ids[:1, :]
							lowerCAmelCase__	 =  input_mask[:1, :]
							lowerCAmelCase__	 =  token_type_ids[:1, :]
							lowerCAmelCase__	 =  1

							# first forward pass
							lowerCAmelCase__	 =  model(__UpperCAmelCase						,      attention_mask=__UpperCAmelCase						,      token_type_ids=__UpperCAmelCase						,      use_cache=__UpperCAmelCase    )

							lowerCAmelCase__ ,							lowerCAmelCase__	 =  outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							lowerCAmelCase__	 =  ids_tensor((self.batch_size, 3)						,      config.vocab_size    )
							lowerCAmelCase__	 =  ids_tensor((self.batch_size, 3)						,      2    )
							lowerCAmelCase__	 =  ids_tensor((self.batch_size, 3)						,      self.type_vocab_size    )

							# append to next input_ids and token_type_ids
							lowerCAmelCase__	 =  tf.concat([input_ids, next_tokens]						,      axis=-1    )
							lowerCAmelCase__	 =  tf.concat([input_mask, next_attn_mask]						,      axis=-1    )
							lowerCAmelCase__	 =  tf.concat([token_type_ids, next_token_types]						,      axis=-1    )

							lowerCAmelCase__	 =  model(
							    __UpperCAmelCase						,      token_type_ids=__UpperCAmelCase						,      attention_mask=__UpperCAmelCase    )["last_hidden_state"]
							lowerCAmelCase__	 =  model(
							    __UpperCAmelCase						,      token_type_ids=__UpperCAmelCase						,      attention_mask=__UpperCAmelCase						,      past_key_values=__UpperCAmelCase						,      )["last_hidden_state"]
							self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]    )

							# select random slice
							lowerCAmelCase__	 =  int(ids_tensor((1,)						,      shape_list(__UpperCAmelCase    )[-1]    )    )
							lowerCAmelCase__	 =  output_from_no_past[:, -3:, random_slice_idx]
							lowerCAmelCase__	 =  output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(__UpperCAmelCase						,      __UpperCAmelCase						,      rtol=1E-3    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      *__UpperCAmelCase    )->   Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJForCausalLM(config=__UpperCAmelCase    )
							lowerCAmelCase__	 =  {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							lowerCAmelCase__	 =  model(__UpperCAmelCase    )
							self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.vocab_size)    )


	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.prepare_config_and_inputs()

							(
							    (
							    lowerCAmelCase__
							) ,							(
							    lowerCAmelCase__
							) ,							(
							    lowerCAmelCase__
							) ,							(
							    lowerCAmelCase__
							) ,							(
							    lowerCAmelCase__
							) ,							(
							    lowerCAmelCase__
							) ,							(
							    lowerCAmelCase__
							) ,							(
							    lowerCAmelCase__
							) ,							(
							    lowerCAmelCase__
							) ,							
							)	 =  config_and_inputs

							lowerCAmelCase__	 =  {
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							    "attention_mask": input_mask,
							}
							return config, inputs_dict





@require_tf
class        lowercase__       (     _UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase,   unittest.TestCase		):
	a_             =(
	    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
	    if is_tf_available()
	    else ()
	)

	a_             =(TFGPTJForCausalLM,) if is_tf_available() else ()
	a_             =(
	    {
	        """feature-extraction""": TFGPTJModel,
	        """question-answering""": TFGPTJForQuestionAnswering,
	        """text-classification""": TFGPTJForSequenceClassification,
	        """text-generation""": TFGPTJForCausalLM,
	        """zero-shot""": TFGPTJForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	a_             =False
	a_             =False
	a_             =False
	a_             =False

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   Any:







							'''simple docstring'''





							if (
							    pipeline_test_casse_name == "QAPipelineTests"
							    and tokenizer_name is not None
							    and not tokenizer_name.endswith("Fast"    )
							):
													# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
													# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
													# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
													return True

							return False

	def 						UpperCAmelCase					(							self    )->   str:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJModelTester(self    )
							lowerCAmelCase__	 =  ConfigTester(self						,      config_class=__UpperCAmelCase						,      n_embd=37    )

	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							self.config_tester.run_common_tests()

	def 						UpperCAmelCase					(							self    )->   List[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_model(*__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self    )->   str:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_model_past(*__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self    )->   List[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_model_attention_mask_past(*__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self    )->   Tuple:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_model_past_large_inputs(*__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self    )->   Tuple:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_gptj_lm_head_model(*__UpperCAmelCase    )

	@slow
	@unittest.skipIf(
	    not is_tf_available() or len(tf.config.list_physical_devices("GPU"    )    ) > 0						,      "skip testing on GPU for now to avoid GPU OOM."						,      )
	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B"						,      from_pt=__UpperCAmelCase    )
							self.assertIsNotNone(__UpperCAmelCase    )


	@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."    )
	def 						UpperCAmelCase					(							self    )->   Dict:







							'''simple docstring'''





							super().test_resize_token_embeddings()





@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class        lowercase__       (     unittest.TestCase		):

	def 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"						,      from_pt=__UpperCAmelCase    )
							lowerCAmelCase__	 =  tf.convert_to_tensor([[464, 3290]]						,      dtype=tf.intaa    )  # The dog
							# fmt: off
							# The dog is a man's best friend. It is a loyal companion, and it is a friend
							lowerCAmelCase__	 =  [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545]
							# fmt: on
							lowerCAmelCase__	 =  model.generate(__UpperCAmelCase						,      do_sample=__UpperCAmelCase    )
							self.assertListEqual(output_ids[0].numpy().tolist()						,      __UpperCAmelCase    )

	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__	 =  AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"						,      revision="float16"    )
							lowerCAmelCase__	 =  TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"						,      revision="float16"						,      from_pt=__UpperCAmelCase    )

							lowerCAmelCase__	 =  tokenizer("Today is a nice day and"						,      return_tensors="tf"    )
							# forces the generation to happen on CPU, to avoid GPU-related quirks
							with tf.device(":/CPU:0"    ):
													lowerCAmelCase__	 =  model.generate(**__UpperCAmelCase						,      do_sample=__UpperCAmelCase						,      seed=[42, 0]    )
							lowerCAmelCase__	 =  tokenizer.decode(output_ids[0]						,      skip_special_tokens=__UpperCAmelCase    )

							lowerCAmelCase__	 =  "Today is a nice day and I’m going to go for a walk. I’"
							self.assertEqual(__UpperCAmelCase						,      __UpperCAmelCase    )

	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"						,      revision="float16"						,      from_pt=__UpperCAmelCase    )
							lowerCAmelCase__	 =  AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"						,      revision="float16"    )

							lowerCAmelCase__	 =  "left"

							# Define PAD Token = EOS Token = 50256
							lowerCAmelCase__	 =  tokenizer.eos_token
							lowerCAmelCase__	 =  model.config.eos_token_id

							# use different length sentences to test batching
							lowerCAmelCase__	 =  [
							    "Hello, my dog is a little",
							    "Today, I",
							]
							lowerCAmelCase__	 =  [
							    "Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia",
							    "Today, I’m going to be talking about a topic that’",
							]
							return model, tokenizer, sentences, expected_output_sentences

	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__	 =  self._get_beam_search_test_objects()

							lowerCAmelCase__	 =  tokenizer(__UpperCAmelCase						,      return_tensors="tf"						,      padding=__UpperCAmelCase    )
							lowerCAmelCase__	 =  model.generate(**__UpperCAmelCase						,      do_sample=__UpperCAmelCase						,      num_beams=2    )
							lowerCAmelCase__	 =  tokenizer.batch_decode(__UpperCAmelCase						,      skip_special_tokens=__UpperCAmelCase    )
							self.assertListEqual(__UpperCAmelCase						,      __UpperCAmelCase    )

	def 						UpperCAmelCase					(							self    )->   Tuple:







							'''simple docstring'''





							lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__	 =  self._get_beam_search_test_objects()

							lowerCAmelCase__	 =  tokenizer(__UpperCAmelCase						,      return_tensors="tf"						,      padding=__UpperCAmelCase    )
							lowerCAmelCase__	 =  tokenizer(sentences[0]						,      return_tensors="tf"    )
							lowerCAmelCase__	 =  model.generate(**__UpperCAmelCase						,      do_sample=__UpperCAmelCase						,      num_beams=2    )
							lowerCAmelCase__	 =  (
							    shape_list(inputs_non_padded["input_ids"]    )[-1]
							    - tf.reduce_sum(tf.cast(inputs["attention_mask"][-1]						,      tf.intaa    )    ).numpy()
							)
							lowerCAmelCase__	 =  tokenizer(sentences[1]						,      return_tensors="tf"    )
							lowerCAmelCase__	 =  model.generate(
							    **__UpperCAmelCase						,      do_sample=__UpperCAmelCase						,      num_beams=2						,      max_length=model.config.max_length - num_paddings    )
							lowerCAmelCase__	 =  tokenizer.decode(output_non_padded[0]						,      skip_special_tokens=__UpperCAmelCase    )
							lowerCAmelCase__	 =  tokenizer.decode(output_padded[0]						,      skip_special_tokens=__UpperCAmelCase    )
							self.assertListEqual(__UpperCAmelCase						,      [non_padded_sentence, padded_sentence]    )


	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__	 =  self._get_beam_search_test_objects()

							lowerCAmelCase__	 =  tokenizer(__UpperCAmelCase						,      return_tensors="tf"						,      padding=__UpperCAmelCase    )
							lowerCAmelCase__	 =  tf.function(model.generate						,      jit_compile=__UpperCAmelCase    )
							lowerCAmelCase__	 =  xla_generate(**__UpperCAmelCase						,      do_sample=__UpperCAmelCase						,      num_beams=2    )
							lowerCAmelCase__	 =  tokenizer.batch_decode(__UpperCAmelCase						,      skip_special_tokens=__UpperCAmelCase    )
							self.assertListEqual(__UpperCAmelCase						,      __UpperCAmelCase    )














from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


a_							=      logging.get_logger(__name__)  # pylint: disable=invalid-name


a_							=      '''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''


class        lowercase__       (     _UpperCAmelCase		):

	def __init__(							self						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   None:







							'''simple docstring'''





							super().__init__()

							self.register_modules(
							    unet=__UpperCAmelCase						,      scheduler=__UpperCAmelCase						,      )

							lowerCAmelCase__	 =  None

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=0    )->   Optional[Any]:







							'''simple docstring'''





							if is_accelerate_available() and is_accelerate_version(">="						,      "0.14.0"    ):
													from accelerate import cpu_offload
							else:
													raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"    )

							lowerCAmelCase__	 =  torch.device(F"cuda:{gpu_id}"    )

							if self.device.type != "cpu":
													self.to("cpu"						,      silence_dtype_warnings=__UpperCAmelCase    )
													torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							for cpu_offloaded_model in [self.unet]:
													cpu_offload(__UpperCAmelCase						,      __UpperCAmelCase    )

							if self.safety_checker is not None:
													cpu_offload(self.safety_checker						,      execution_device=__UpperCAmelCase						,      offload_buffers=__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=0    )->   Any:







							'''simple docstring'''





							if is_accelerate_available() and is_accelerate_version(">="						,      "0.17.0.dev0"    ):
													from accelerate import cpu_offload_with_hook
							else:
													raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."    )

							lowerCAmelCase__	 =  torch.device(F"cuda:{gpu_id}"    )

							if self.device.type != "cpu":
													self.to("cpu"						,      silence_dtype_warnings=__UpperCAmelCase    )
													torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							lowerCAmelCase__	 =  None
							for cpu_offloaded_model in [self.unet]:
													lowerCAmelCase__ ,							lowerCAmelCase__	 =  cpu_offload_with_hook(__UpperCAmelCase						,      __UpperCAmelCase						,      prev_module_hook=__UpperCAmelCase    )

							if self.safety_checker is not None:
													lowerCAmelCase__ ,							lowerCAmelCase__	 =  cpu_offload_with_hook(self.safety_checker						,      __UpperCAmelCase						,      prev_module_hook=__UpperCAmelCase    )

							# We'll offload the last model manually.
							lowerCAmelCase__	 =  hook

	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							if not hasattr(self.unet						,      "_hf_hook"    ):
													return self.device
							for module in self.unet.modules():
													if (
													    hasattr(__UpperCAmelCase						,      "_hf_hook"    )
													    and hasattr(module._hf_hook						,      "execution_device"    )
													    and module._hf_hook.execution_device is not None
													):
																			return torch.device(module._hf_hook.execution_device    )
							return self.device

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase=None    )->   Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  (batch_size, num_channels, height, width)
							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ) and len(__UpperCAmelCase    ) != batch_size:
													raise ValueError(
													    F"You have passed a list of generators of length {len(__UpperCAmelCase    )}, but requested an effective batch"
													    F" size of {batch_size}. Make sure the batch size matches the length of the generators."    )

							if latents is None:
													lowerCAmelCase__	 =  randn_tensor(__UpperCAmelCase						,      generator=__UpperCAmelCase						,      device=__UpperCAmelCase						,      dtype=__UpperCAmelCase    )
							else:
													lowerCAmelCase__	 =  latents.to(device=__UpperCAmelCase						,      dtype=__UpperCAmelCase    )

							# scale the initial noise by the standard deviation required by the scheduler
							lowerCAmelCase__	 =  latents * self.scheduler.init_noise_sigma
							return latents

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase = "pil"    )->   Tuple:







							'''simple docstring'''





							if output_type not in ["pt", "np", "pil"]:
													raise ValueError(
													    F"output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"    )

							# Equivalent to diffusers.VaeImageProcessor.denormalize
							lowerCAmelCase__	 =  (sample / 2 + 0.5).clamp(0						,      1    )
							if output_type == "pt":
													return sample

							# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
							lowerCAmelCase__	 =  sample.cpu().permute(0						,      2						,      3						,      1    ).numpy()
							if output_type == "np":
													return sample

							# Output_type must be 'pil'
							lowerCAmelCase__	 =  self.numpy_to_pil(__UpperCAmelCase    )
							return sample

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase=None    )->   List[Any]:







							'''simple docstring'''





							if self.unet.config.num_class_embeds is not None:
													if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
																			lowerCAmelCase__	 =  torch.tensor(__UpperCAmelCase						,      dtype=torch.int    )
													elif isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
																			assert batch_size == 1, "Batch size must be 1 if classes is an int"
																			lowerCAmelCase__	 =  torch.tensor([class_labels]						,      dtype=torch.int    )
													elif class_labels is None:
																			# Randomly generate batch_size class labels
																			# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
																			lowerCAmelCase__	 =  torch.randint(0						,      self.unet.config.num_class_embeds						,      size=(batch_size,)    )
													lowerCAmelCase__	 =  class_labels.to(__UpperCAmelCase    )
							else:
													lowerCAmelCase__	 =  None
							return class_labels

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   List[Any]:







							'''simple docstring'''





							if num_inference_steps is None and timesteps is None:
													raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."    )

							if num_inference_steps is not None and timesteps is not None:
													logger.warning(
													    F"Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"
													    " `timesteps` will be used over `num_inference_steps`."    )

							if latents is not None:
													lowerCAmelCase__	 =  (batch_size, 3, img_size, img_size)
													if latents.shape != expected_shape:
																			raise ValueError(F"The shape of latents is {latents.shape} but is expected to be {expected_shape}."    )

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ) or callback_steps <= 0)
							):
													raise ValueError(
													    F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
													    F" {type(__UpperCAmelCase    )}."    )


	@torch.no_grad()
	@replace_example_docstring(__UpperCAmelCase    )
	def __call__(							self						,      __UpperCAmelCase = 1						,      __UpperCAmelCase = None						,      __UpperCAmelCase = 1						,      __UpperCAmelCase = None						,      __UpperCAmelCase = None						,      __UpperCAmelCase = None						,      __UpperCAmelCase = "pil"						,      __UpperCAmelCase = True						,      __UpperCAmelCase = None						,      __UpperCAmelCase = 1						,      )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.unet.config.sample_size
							lowerCAmelCase__	 =  self._execution_device

							# 1. Check inputs
							self.check_inputs(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )

							# 2. Prepare image latents
							# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
							lowerCAmelCase__	 =  self.prepare_latents(
							    batch_size=__UpperCAmelCase						,      num_channels=self.unet.config.in_channels						,      height=__UpperCAmelCase						,      width=__UpperCAmelCase						,      dtype=self.unet.dtype						,      device=__UpperCAmelCase						,      generator=__UpperCAmelCase						,      latents=__UpperCAmelCase						,      )

							# 3. Handle class_labels for class-conditional models
							lowerCAmelCase__	 =  self.prepare_class_labels(__UpperCAmelCase						,      __UpperCAmelCase						,      class_labels=__UpperCAmelCase    )

							# 4. Prepare timesteps
							if timesteps is not None:
													self.scheduler.set_timesteps(timesteps=__UpperCAmelCase						,      device=__UpperCAmelCase    )
													lowerCAmelCase__	 =  self.scheduler.timesteps
													lowerCAmelCase__	 =  len(__UpperCAmelCase    )
							else:
													self.scheduler.set_timesteps(__UpperCAmelCase    )
													lowerCAmelCase__	 =  self.scheduler.timesteps

							# 5. Denoising loop
							# Multistep sampling: implements Algorithm 1 in the paper
							with self.progress_bar(total=__UpperCAmelCase    ) as progress_bar:
													for i, t in enumerate(__UpperCAmelCase    ):
																			lowerCAmelCase__	 =  self.scheduler.scale_model_input(__UpperCAmelCase						,      __UpperCAmelCase    )
																			lowerCAmelCase__	 =  self.unet(__UpperCAmelCase						,      __UpperCAmelCase						,      class_labels=__UpperCAmelCase						,      return_dict=__UpperCAmelCase    )[0]

																			lowerCAmelCase__	 =  self.scheduler.step(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      generator=__UpperCAmelCase    )[0]

																			# call the callback, if provided
																			progress_bar.update()
																			if callback is not None and i % callback_steps == 0:
																									callback(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )

        # 6. Post-process image sample
							lowerCAmelCase__	 =  self.postprocess_image(__UpperCAmelCase						,      output_type=__UpperCAmelCase    )

							# Offload last model to CPU
							if hasattr(self						,      "final_offload_hook"    ) and self.final_offload_hook is not None:
													self.final_offload_hook.offload()

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=__UpperCAmelCase    )






