import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

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


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					__a  :					Any          =  """platform"""

					import jax
					import jax.numpy as jnp

					from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase=None							,      lowercase=None							):







				"""simple docstring"""



				if attention_mask is None:
								__lowercase			      =							np.where(input_ids != config.pad_token_id							,      1							,      0							)
				return {
				    "input_ids": input_ids,
				    "attention_mask": attention_mask,
				}




@require_flax
class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__=13				,       lowerCAmelCase__=7				,       lowerCAmelCase__=True				,       lowerCAmelCase__=False				,       lowerCAmelCase__=99				,       lowerCAmelCase__=16				,       lowerCAmelCase__=2				,       lowerCAmelCase__=4				,       lowerCAmelCase__=4				,       lowerCAmelCase__="gelu"				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=20				,       lowerCAmelCase__=2				,       lowerCAmelCase__=1				,       lowerCAmelCase__=0				,       lowerCAmelCase__=16				,       lowerCAmelCase__=16				,       lowerCAmelCase__=0.02				,       )					-> Any:

							'''simple docstring'''


							__lowercase			      =							parent
							__lowercase			      =							batch_size
							__lowercase			      =							seq_length
							__lowercase			      =							is_training
							__lowercase			      =							use_labels
							__lowercase			      =							vocab_size
							__lowercase			      =							hidden_size
							__lowercase			      =							num_hidden_layers
							__lowercase			      =							num_attention_heads
							__lowercase			      =							intermediate_size
							__lowercase			      =							hidden_act
							__lowercase			      =							hidden_dropout_prob
							__lowercase			      =							attention_probs_dropout_prob
							__lowercase			      =							max_position_embeddings
							__lowercase			      =							eos_token_id
							__lowercase			      =							pad_token_id
							__lowercase			      =							bos_token_id
							__lowercase			      =							embed_dim
							__lowercase			      =							word_embed_proj_dim
							__lowercase			      =							initializer_range
							__lowercase			      =							False





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

							'''simple docstring'''


							__lowercase			      =							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,       self.vocab_size						)				,       3				,       self.vocab_size						)
							__lowercase			      =							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,       dtype=np.intaa						))				,       -1						)

							__lowercase			      =							OPTConfig(
							    vocab_size=self.vocab_size				,       hidden_size=self.hidden_size				,       num_hidden_layers=self.num_hidden_layers				,       num_attention_heads=self.num_attention_heads				,       ffn_dim=self.intermediate_size				,       dropout=self.hidden_dropout_prob				,       attention_dropout=self.attention_probs_dropout_prob				,       max_position_embeddings=self.max_position_embeddings				,       eos_token_id=self.eos_token_id				,       bos_token_id=self.bos_token_id				,       pad_token_id=self.pad_token_id				,       embed_dim=self.embed_dim				,       is_encoder_decoder=lowerCAmelCase__				,       word_embed_proj_dim=self.word_embed_proj_dim				,       initializer_range=self.initializer_range				,       use_cache=lowerCAmelCase__				,       )
							__lowercase			      =							prepare_opt_inputs_dict(lowerCAmelCase__				,       lowerCAmelCase__						)
							return config, inputs_dict





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

							'''simple docstring'''


							__lowercase      ,					__lowercase			      =							self.prepare_config_and_inputs()
							return config, inputs_dict





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							20
							__lowercase			      =							model_class_name(lowerCAmelCase__						)

							__lowercase			      =							inputs_dict['''input_ids''']
							__lowercase			      =							inputs_dict['''attention_mask''']

							__lowercase			      =							model.init_cache(input_ids.shape[0]				,       lowerCAmelCase__						)
							__lowercase			      =							jnp.ones((input_ids.shape[0], max_length)				,       dtype='''i4'''						)

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

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

							__lowercase			      =							model(lowerCAmelCase__						)

							__lowercase			      =							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 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


							__lowercase			      =							20
							__lowercase			      =							model_class_name(lowerCAmelCase__						)

							__lowercase      ,					__lowercase			      =							(
							    inputs_dict['''input_ids'''],
							    inputs_dict['''attention_mask'''],
							)

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

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

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

							__lowercase			      =							model(lowerCAmelCase__				,       attention_mask=lowerCAmelCase__						)

							__lowercase			      =							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        _UpperCamelCase				(						_UpperCAmelCase						,unittest.TestCase						,_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			int      			=						(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
			__a  :			Dict      			=						() if is_flax_available() else ()





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

							'''simple docstring'''


							__lowercase			      =							FlaxOPTModelTester(self						)





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

							'''simple docstring'''


							__lowercase      ,					__lowercase			      =							self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase      ,					__lowercase			      =							self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)





			@slow
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> str:

							'''simple docstring'''


							for model_class_name in self.all_model_classes:
											__lowercase			      =							model_class_name.from_pretrained('''facebook/opt-125m'''						)
											__lowercase			      =							np.ones((1, 1)						) * model.config.eos_token_id
											__lowercase			      =							model(lowerCAmelCase__						)
											self.assertIsNotNone(lowerCAmelCase__						)




@require_sentencepiece
@require_flax
class        _UpperCamelCase				(						unittest.TestCase			):


			"""simple docstring"""






			@slow
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							FlaxOPTModel.from_pretrained('''facebook/opt-350m'''						)
							__lowercase			      =							jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]						)
							__lowercase			      =							model(input_ids=lowerCAmelCase__						).last_hidden_state
							__lowercase			      =							(1, 11, 5_12)
							self.assertEqual(output.shape				,       lowerCAmelCase__						)
							__lowercase			      =							jnp.array(
							    [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]						)
							self.assertTrue(jnp.allclose(output[:, :3, :3]				,       lowerCAmelCase__				,       atol=4E-2						)						)




@require_flax
@slow
class        _UpperCamelCase				(						unittest.TestCase			):


			"""simple docstring"""






			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							super().setUp()
							__lowercase			      =							'''facebook/opt-350m'''





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

							'''simple docstring'''


							__lowercase			      =							FlaxOPTForCausalLM.from_pretrained(self.path_model						)
							__lowercase			      =							GPTaTokenizer.from_pretrained(self.path_model						)

							__lowercase			      =							[
							    '''Today is a beautiful day and I want to''',
							    '''In the city of''',
							    '''Paris is the capital of France and''',
							    '''Computers and mobile phones have taken''',
							]
							# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
							__lowercase			      =							tokenizer(lowerCAmelCase__				,       return_tensors='''jax'''				,       padding=lowerCAmelCase__				,       add_special_tokens=lowerCAmelCase__						)
							__lowercase			      =							model(inputs.input_ids				,       attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
							__lowercase			      =							jnp.array(
							    [
							        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
							        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
							        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
							        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
							    ]						)
							self.assertTrue(jnp.allclose(lowerCAmelCase__				,       lowerCAmelCase__				,       atol=4E-2						)						)

							__lowercase			      =							jax.jit(lowerCAmelCase__						)
							__lowercase			      =							model(inputs.input_ids				,       attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
							self.assertTrue(jnp.allclose(lowerCAmelCase__				,       lowerCAmelCase__				,       atol=4E-2						)						)




@require_flax
@slow
class        _UpperCamelCase				(						unittest.TestCase			):


			"""simple docstring"""






			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''simple docstring'''


							return [
							    "Today is a beautiful day and I want",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]





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

							'''simple docstring'''


							__lowercase			      =							'''facebook/opt-125m'''

							__lowercase			      =							[
							    '''Today is a beautiful day and I want to''',
							    '''In the city of New York, the city''',
							    '''Paris is the capital of France and the capital''',
							    '''Computers and mobile phones have taken over the''',
							]

							__lowercase			      =							[]

							__lowercase			      =							FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__						)
							__lowercase			      =							GPTaTokenizer.from_pretrained(lowerCAmelCase__						)

							for prompt in self.prompts:
											__lowercase			      =							tokenizer(lowerCAmelCase__				,       return_tensors='''jax'''						).input_ids

											__lowercase			      =							model.generate(lowerCAmelCase__				,       max_length=10						)
											__lowercase			      =							generated_ids[0]

											__lowercase			      =							tokenizer.batch_decode(lowerCAmelCase__				,       skip_special_tokens=lowerCAmelCase__						)
											predicted_outputs += generated_string

							self.assertListEqual(lowerCAmelCase__				,       lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							__lowercase			      =							'''facebook/opt-350m'''

							__lowercase			      =							[
							    '''Today is a beautiful day and I want to''',
							    '''In the city of San Francisco, the city''',
							    '''Paris is the capital of France and the capital''',
							    '''Computers and mobile phones have taken over the''',
							]

							__lowercase			      =							[]
							__lowercase			      =							FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__						)
							__lowercase			      =							GPTaTokenizer.from_pretrained(lowerCAmelCase__						)

							for prompt in self.prompts:
											__lowercase			      =							tokenizer(lowerCAmelCase__				,       return_tensors='''jax'''						).input_ids

											__lowercase			      =							model.generate(lowerCAmelCase__				,       max_length=10						)
											__lowercase			      =							generated_ids[0]

											__lowercase			      =							tokenizer.batch_decode(lowerCAmelCase__				,       skip_special_tokens=lowerCAmelCase__						)
											predicted_outputs += generated_string

							self.assertListEqual(lowerCAmelCase__				,       lowerCAmelCase__						)





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

							'''simple docstring'''


							__lowercase			      =							'''facebook/opt-125m'''
							__lowercase			      =							[
							    '''Today is a beautiful day and I want to thank''',
							    '''In the city of Rome Canaver Canaver Canaver Canaver''',
							]
							__lowercase			      =							FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__						)
							__lowercase			      =							GPTaTokenizer.from_pretrained(lowerCAmelCase__						)
							__lowercase			      =							tokenizer(
							    [
							        '''Today is a beautiful day and I want to''',
							        '''In the city of''',
							    ]				,       return_tensors='''jax'''				,       padding=lowerCAmelCase__				,       )

							__lowercase			      =							jax.jit(model.generate						)

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

							__lowercase			      =							tokenizer.batch_decode(lowerCAmelCase__				,       skip_special_tokens=lowerCAmelCase__						)

							self.assertIsNotNone(lowerCAmelCase__				,       lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							'''facebook/opt-350m'''

							__lowercase			      =							GPTaTokenizer.from_pretrained(lowerCAmelCase__						)
							__lowercase			      =							FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__						)

							__lowercase			      =							'''left'''

							# use different length sentences to test batching
							__lowercase			      =							[
							    '''Hello, my dog is a little''',
							    '''Today, I''',
							]

							__lowercase			      =							tokenizer(lowerCAmelCase__				,       return_tensors='''jax'''				,       padding=lowerCAmelCase__						)
							__lowercase			      =							inputs['''input_ids''']

							__lowercase			      =							model.generate(input_ids=lowerCAmelCase__				,       attention_mask=inputs['''attention_mask''']				,       trace=lowerCAmelCase__						)

							__lowercase			      =							tokenizer(sentences[0]				,       return_tensors='''jax'''						).input_ids
							__lowercase			      =							model.generate(input_ids=lowerCAmelCase__						)

							__lowercase			      =							inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
							__lowercase			      =							tokenizer(sentences[1]				,       return_tensors='''jax'''						).input_ids
							__lowercase			      =							model.generate(input_ids=lowerCAmelCase__				,       max_length=model.config.max_length - num_paddings						)

							__lowercase			      =							tokenizer.batch_decode(outputs[0]				,       skip_special_tokens=lowerCAmelCase__						)
							__lowercase			      =							tokenizer.decode(output_non_padded[0][0]				,       skip_special_tokens=lowerCAmelCase__						)
							__lowercase			      =							tokenizer.decode(output_padded[0][0]				,       skip_special_tokens=lowerCAmelCase__						)

							__lowercase			      =							[
							    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
							    '''Today, I was in the middle of a conversation with a friend about the''',
							]
							self.assertListEqual(lowerCAmelCase__				,       lowerCAmelCase__						)
							self.assertListEqual(lowerCAmelCase__				,       [non_padded_sentence, padded_sentence]						)
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


__a  :					int          =  logging.get_logger(__name__)

__a  :					Optional[Any]          =  {
    """microsoft/layoutlm-base-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"""
    ),
    """microsoft/layoutlm-large-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"""
    ),
}




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			List[Any]      			=						'''layoutlm'''





			def __init__(       self				,       lowerCAmelCase__=3_05_22				,       lowerCAmelCase__=7_68				,       lowerCAmelCase__=12				,       lowerCAmelCase__=12				,       lowerCAmelCase__=30_72				,       lowerCAmelCase__="gelu"				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=5_12				,       lowerCAmelCase__=2				,       lowerCAmelCase__=0.02				,       lowerCAmelCase__=1E-12				,       lowerCAmelCase__=0				,       lowerCAmelCase__="absolute"				,       lowerCAmelCase__=True				,       lowerCAmelCase__=10_24				,       **lowerCAmelCase__				,       )					-> Tuple:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__				,       **lowerCAmelCase__						)
							__lowercase			      =							vocab_size
							__lowercase			      =							hidden_size
							__lowercase			      =							num_hidden_layers
							__lowercase			      =							num_attention_heads
							__lowercase			      =							hidden_act
							__lowercase			      =							intermediate_size
							__lowercase			      =							hidden_dropout_prob
							__lowercase			      =							attention_probs_dropout_prob
							__lowercase			      =							max_position_embeddings
							__lowercase			      =							type_vocab_size
							__lowercase			      =							initializer_range
							__lowercase			      =							layer_norm_eps
							__lowercase			      =							position_embedding_type
							__lowercase			      =							use_cache
							__lowercase			      =							max_ad_position_embeddings




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = "default"				,       lowerCAmelCase__ = None				,       )					-> str:

							'''simple docstring'''


							super().__init__(lowerCAmelCase__				,       task=lowerCAmelCase__				,       patching_specs=lowerCAmelCase__						)
							__lowercase			      =							config.max_ad_position_embeddings - 1





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							return OrderedDict(
							    [
							        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
							        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
							        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
							        ('''token_type_ids''', {0: '''batch''', 1: '''sequence'''}),
							    ]						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = False				,       lowerCAmelCase__ = None				,       )					-> Mapping[str, Any]:

							'''simple docstring'''


							__lowercase			      =							super().generate_dummy_inputs(
							    lowerCAmelCase__				,       batch_size=lowerCAmelCase__				,       seq_length=lowerCAmelCase__				,       is_pair=lowerCAmelCase__				,       framework=lowerCAmelCase__						)

							# Generate a dummy bbox
							__lowercase			      =							[48, 84, 73, 1_28]

							if not framework == TensorType.PYTORCH:
											raise NotImplementedError('''Exporting LayoutLM to ONNX is currently only supported for PyTorch.'''						)

							if not is_torch_available():
											raise ValueError('''Cannot generate dummy inputs without PyTorch installed.'''						)
							import torch

							__lowercase      ,					__lowercase			      =							input_dict['''input_ids'''].shape
							__lowercase			      =							torch.tensor([*[box] * seq_length]						).tile(lowerCAmelCase__				,       1				,       1						)
							return input_dict