






"""simple docstring"""




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
		import jax
		import jax.numpy as jnp

		from transformers.modeling_flax_pytorch_utils import (
		    convert_pytorch_state_dict_to_flax,
		    load_flax_weights_in_pytorch_model,
		)
		from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
		import torch





class __lowerCamelCase			:





	"""simple docstring"""






	def __init__(							self	,   UpperCAmelCase	,   UpperCAmelCase=14	,   UpperCAmelCase=7	,   UpperCAmelCase=True	,   UpperCAmelCase=True	,   UpperCAmelCase=False	,   UpperCAmelCase=True	,   UpperCAmelCase=99	,   UpperCAmelCase=32	,   UpperCAmelCase=5	,   UpperCAmelCase=4	,   UpperCAmelCase=37	,   UpperCAmelCase="gelu"	,   UpperCAmelCase=0.1	,   UpperCAmelCase=0.1	,   UpperCAmelCase=512	,   UpperCAmelCase=0.02	,   ):
							"""simple docstring"""







							_UpperCAmelCase     =      parent
							_UpperCAmelCase     =      batch_size
							_UpperCAmelCase     =      seq_length
							_UpperCAmelCase     =      is_training
							_UpperCAmelCase     =      use_input_mask
							_UpperCAmelCase     =      use_token_type_ids
							_UpperCAmelCase     =      use_labels
							_UpperCAmelCase     =      vocab_size
							_UpperCAmelCase     =      hidden_size
							_UpperCAmelCase     =      num_hidden_layers
							_UpperCAmelCase     =      num_attention_heads
							_UpperCAmelCase     =      intermediate_size
							_UpperCAmelCase     =      hidden_act
							_UpperCAmelCase     =      hidden_dropout_prob
							_UpperCAmelCase     =      attention_probs_dropout_prob
							_UpperCAmelCase     =      max_position_embeddings
							_UpperCAmelCase     =      initializer_range
							_UpperCAmelCase     =      None
							_UpperCAmelCase     =      vocab_size - 1
							_UpperCAmelCase     =      vocab_size - 1
							_UpperCAmelCase     =      vocab_size - 1

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







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

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

							_UpperCAmelCase     =      GPTaConfig(
							    vocab_size=self.vocab_size	,   n_embd=self.hidden_size	,   n_layer=self.num_hidden_layers	,   n_head=self.num_attention_heads	,   n_positions=self.max_position_embeddings	,   use_cache=UpperCAmelCase	,   bos_token_id=self.bos_token_id	,   eos_token_id=self.eos_token_id	,   pad_token_id=self.pad_token_id	,   )

							return (config, input_ids, input_mask)

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.prepare_config_and_inputs()
							_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase     =      config_and_inputs
							_UpperCAmelCase     =      {'input_ids': input_ids, 'attention_mask': attention_mask}
							return config, inputs_dict

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase     =      self.prepare_config_and_inputs()

							_UpperCAmelCase     =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
							_UpperCAmelCase     =      ids_tensor([self.batch_size, self.seq_length]	,   vocab_size=2   )

							return (
							    config,
							    input_ids,
							    attention_mask,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      20
							_UpperCAmelCase     =      model_class_name(UpperCAmelCase   )

							_UpperCAmelCase     =      model.init_cache(input_ids.shape[0]	,   UpperCAmelCase   )
							_UpperCAmelCase     =      jnp.ones((input_ids.shape[0], max_decoder_length)	,   dtype='i4'   )

							_UpperCAmelCase     =      jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1   )[None, :]	,   (input_ids.shape[0], input_ids.shape[-1] - 1)   )
							_UpperCAmelCase     =      model(
							    input_ids[:, :-1]	,   attention_mask=UpperCAmelCase	,   past_key_values=UpperCAmelCase	,   position_ids=UpperCAmelCase	,   )

							_UpperCAmelCase     =      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,   dtype='i4'   )
							_UpperCAmelCase     =      model(
							    input_ids[:, -1:]	,   attention_mask=UpperCAmelCase	,   past_key_values=outputs_cache.past_key_values	,   position_ids=UpperCAmelCase	,   )

							_UpperCAmelCase     =      model(UpperCAmelCase   )

							_UpperCAmelCase     =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
							self.parent.assertTrue(diff < 1e-3	,   msg=F"""Max diff is {diff}"""   )

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      20
							_UpperCAmelCase     =      model_class_name(UpperCAmelCase   )

							_UpperCAmelCase     =      jnp.concatenate(
							    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])   )]	,   axis=-1	,   )

							_UpperCAmelCase     =      model.init_cache(input_ids.shape[0]	,   UpperCAmelCase   )
							_UpperCAmelCase     =      jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1   )[None, :]	,   (input_ids.shape[0], input_ids.shape[-1] - 1)   )

							_UpperCAmelCase     =      model(
							    input_ids[:, :-1]	,   attention_mask=UpperCAmelCase	,   past_key_values=UpperCAmelCase	,   position_ids=UpperCAmelCase	,   )
							_UpperCAmelCase     =      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,   dtype='i4'   )
							_UpperCAmelCase     =      model(
							    input_ids[:, -1:]	,   past_key_values=outputs_cache.past_key_values	,   attention_mask=UpperCAmelCase	,   position_ids=UpperCAmelCase	,   )

							_UpperCAmelCase     =      model(UpperCAmelCase	,   attention_mask=UpperCAmelCase   )

							_UpperCAmelCase     =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
							self.parent.assertTrue(diff < 1e-3	,   msg=F"""Max diff is {diff}"""   )





@require_flax
class __lowerCamelCase			(				snake_case__					,	snake_case__					,	unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=				(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
	UpperCamelCase__								=				(FlaxGPTaLMHeadModel,) if is_flax_available() else ()

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      FlaxGPTaModelTester(self   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							for model_class_name in self.all_model_classes:
													_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase     =      self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward(UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							for model_class_name in self.all_model_classes:
													_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase     =      self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward_with_attn_mask(
													    UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   )

	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      GPTaTokenizer.from_pretrained('gpt2'	,   pad_token='</s>'	,   padding_side='left'   )
							_UpperCAmelCase     =      tokenizer(['Hello this is a long string', 'Hey']	,   return_tensors='np'	,   padding=UpperCAmelCase	,   truncation=UpperCAmelCase   )

							_UpperCAmelCase     =      FlaxGPTaLMHeadModel.from_pretrained('gpt2'   )
							_UpperCAmelCase     =      False
							_UpperCAmelCase     =      model.config.eos_token_id

							_UpperCAmelCase     =      jax.jit(model.generate   )

							_UpperCAmelCase     =      jit_generate(inputs['input_ids']	,   attention_mask=inputs['attention_mask']   ).sequences

							_UpperCAmelCase     =      tokenizer.batch_decode(UpperCAmelCase	,   skip_special_tokens=UpperCAmelCase   )

							_UpperCAmelCase     =      [
							    'Hello this is a long string of words. I\'m going to start with the first one.\n',
							    'Hey, I\'m not sure if I\'m going to be able to do',
							]

							self.assertListEqual(UpperCAmelCase	,   UpperCAmelCase   )

	@is_pt_flax_cross_test
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase ,			_UpperCAmelCase     =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__   ):
																			# prepare inputs
																			_UpperCAmelCase     =      self._prepare_for_class(UpperCAmelCase	,   UpperCAmelCase   )
																			_UpperCAmelCase     =      {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

																			# load corresponding PyTorch class
																			_UpperCAmelCase     =      model_class.__name__[4:]  # Skip the "Flax" at the beginning
																			_UpperCAmelCase     =      getattr(UpperCAmelCase	,   UpperCAmelCase   )

																			_UpperCAmelCase ,			_UpperCAmelCase     =      pt_inputs['input_ids'].shape
																			_UpperCAmelCase     =      np.random.randint(0	,   seq_length - 1	,   size=(batch_size,)   )
																			for batch_idx, start_index in enumerate(UpperCAmelCase   ):
																									_UpperCAmelCase     =      0
																									_UpperCAmelCase     =      1
																									_UpperCAmelCase     =      0
																									_UpperCAmelCase     =      1
																			_UpperCAmelCase     =      pt_model_class(UpperCAmelCase   ).eval()
																			_UpperCAmelCase     =      model_class(UpperCAmelCase	,   dtype=jnp.floataa   )

																			_UpperCAmelCase     =      convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,   UpperCAmelCase   )
																			_UpperCAmelCase     =      fx_state

																			with torch.no_grad():
																									_UpperCAmelCase     =      pt_model(**UpperCAmelCase   ).to_tuple()

																			_UpperCAmelCase     =      fx_model(**UpperCAmelCase   ).to_tuple()
																			self.assertEqual(len(UpperCAmelCase   )	,   len(UpperCAmelCase   )	,   'Output lengths differ between Flax and PyTorch'   )
																			for fx_output, pt_output in zip(UpperCAmelCase	,   UpperCAmelCase   ):
																									self.assert_almost_equals(fx_output[:, -1]	,   pt_output[:, -1].numpy()	,   4e-2   )

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									pt_model.save_pretrained(UpperCAmelCase   )
																									_UpperCAmelCase     =      model_class.from_pretrained(UpperCAmelCase	,   from_pt=UpperCAmelCase   )

																			_UpperCAmelCase     =      fx_model_loaded(**UpperCAmelCase   ).to_tuple()
																			self.assertEqual(
																			    len(UpperCAmelCase   )	,   len(UpperCAmelCase   )	,   'Output lengths differ between Flax and PyTorch'   )
																			for fx_output_loaded, pt_output in zip(UpperCAmelCase	,   UpperCAmelCase   ):
																									self.assert_almost_equals(fx_output_loaded[:, -1]	,   pt_output[:, -1].numpy()	,   4e-2   )

	@is_pt_flax_cross_test
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase ,			_UpperCAmelCase     =      self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__   ):
																			# prepare inputs
																			_UpperCAmelCase     =      self._prepare_for_class(UpperCAmelCase	,   UpperCAmelCase   )
																			_UpperCAmelCase     =      {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

																			# load corresponding PyTorch class
																			_UpperCAmelCase     =      model_class.__name__[4:]  # Skip the "Flax" at the beginning
																			_UpperCAmelCase     =      getattr(UpperCAmelCase	,   UpperCAmelCase   )

																			_UpperCAmelCase     =      pt_model_class(UpperCAmelCase   ).eval()
																			_UpperCAmelCase     =      model_class(UpperCAmelCase	,   dtype=jnp.floataa   )

																			_UpperCAmelCase     =      load_flax_weights_in_pytorch_model(UpperCAmelCase	,   fx_model.params   )
																			_UpperCAmelCase ,			_UpperCAmelCase     =      pt_inputs['input_ids'].shape
																			_UpperCAmelCase     =      np.random.randint(0	,   seq_length - 1	,   size=(batch_size,)   )
																			for batch_idx, start_index in enumerate(UpperCAmelCase   ):
																									_UpperCAmelCase     =      0
																									_UpperCAmelCase     =      1
																									_UpperCAmelCase     =      0
																									_UpperCAmelCase     =      1

																			# make sure weights are tied in PyTorch
																			pt_model.tie_weights()

																			with torch.no_grad():
																									_UpperCAmelCase     =      pt_model(**UpperCAmelCase   ).to_tuple()

																			_UpperCAmelCase     =      fx_model(**UpperCAmelCase   ).to_tuple()
																			self.assertEqual(len(UpperCAmelCase   )	,   len(UpperCAmelCase   )	,   'Output lengths differ between Flax and PyTorch'   )
																			for fx_output, pt_output in zip(UpperCAmelCase	,   UpperCAmelCase   ):
																									self.assert_almost_equals(fx_output[:, -1]	,   pt_output[:, -1].numpy()	,   4e-2   )

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									fx_model.save_pretrained(UpperCAmelCase   )
																									_UpperCAmelCase     =      pt_model_class.from_pretrained(UpperCAmelCase	,   from_flax=UpperCAmelCase   )

																			with torch.no_grad():
																									_UpperCAmelCase     =      pt_model_loaded(**UpperCAmelCase   ).to_tuple()

																			self.assertEqual(
																			    len(UpperCAmelCase   )	,   len(UpperCAmelCase   )	,   'Output lengths differ between Flax and PyTorch'   )
																			for fx_output, pt_output in zip(UpperCAmelCase	,   UpperCAmelCase   ):
																									self.assert_almost_equals(fx_output[:, -1]	,   pt_output[:, -1].numpy()	,   4e-2   )

	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							for model_class_name in self.all_model_classes:
													_UpperCAmelCase     =      model_class_name.from_pretrained('gpt2'	,   from_pt=UpperCAmelCase   )
													_UpperCAmelCase     =      model(np.ones((1, 1)   )   )
													self.assertIsNotNone(UpperCAmelCase   )








import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference





@slow
@require_torch_gpu
class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'runwayml/stable-diffusion-v1-5'
							_UpperCAmelCase     =      TextToVideoZeroPipeline.from_pretrained(UpperCAmelCase	,   torch_dtype=torch.floataa   ).to('cuda'   )
							_UpperCAmelCase     =      DDIMScheduler.from_config(pipe.scheduler.config   )
							_UpperCAmelCase     =      torch.Generator(device='cuda'   ).manual_seed(0   )

							_UpperCAmelCase     =      'A bear is playing a guitar on Times Square'
							_UpperCAmelCase     =      pipe(prompt=UpperCAmelCase	,   generator=UpperCAmelCase   ).images

							_UpperCAmelCase     =      load_pt(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'   )

							assert_mean_pixel_difference(UpperCAmelCase	,   UpperCAmelCase   )
