
'''simple docstring'''
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
				__lowerCAmelCase  							=					'platform'

				import jax
				import jax.numpy as jnp

				from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






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


@require_flax
class     _lowerCAmelCase					:







							'''simple docstring'''

							def __init__(self						,						UpperCAmelCase						,						UpperCAmelCase=13						,						UpperCAmelCase=7						,						UpperCAmelCase=True						,						UpperCAmelCase=False						,						UpperCAmelCase=99						,						UpperCAmelCase=16						,						UpperCAmelCase=2						,						UpperCAmelCase=4						,						UpperCAmelCase=4						,						UpperCAmelCase="gelu"						,						UpperCAmelCase=0.1						,						UpperCAmelCase=0.1						,						UpperCAmelCase=20						,						UpperCAmelCase=2						,						UpperCAmelCase=1						,						UpperCAmelCase=0						,						UpperCAmelCase=16						,						UpperCAmelCase=16						,						UpperCAmelCase=0.02						,						)  ->     str:
														_snake_case     =						parent
														_snake_case     =						batch_size
														_snake_case     =						seq_length
														_snake_case     =						is_training
														_snake_case     =						use_labels
														_snake_case     =						vocab_size
														_snake_case     =						hidden_size
														_snake_case     =						num_hidden_layers
														_snake_case     =						num_attention_heads
														_snake_case     =						intermediate_size
														_snake_case     =						hidden_act
														_snake_case     =						hidden_dropout_prob
														_snake_case     =						attention_probs_dropout_prob
														_snake_case     =						max_position_embeddings
														_snake_case     =						eos_token_id
														_snake_case     =						pad_token_id
														_snake_case     =						bos_token_id
														_snake_case     =						embed_dim
														_snake_case     =						word_embed_proj_dim
														_snake_case     =						initializer_range
														_snake_case     =						False

							def  lowercase							(self						)  ->     int:
														_snake_case     =						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]						,						self.vocab_size						)						,						3						,						self.vocab_size						)
														_snake_case     =						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)						,						dtype=np.intaa						))						,						-1						)

														_snake_case     =						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=lowercase_						,						word_embed_proj_dim=self.word_embed_proj_dim						,						initializer_range=self.initializer_range						,						use_cache=lowercase_						,						)
														_snake_case     =						prepare_opt_inputs_dict(lowercase_						,						lowercase_						)
														return config, inputs_dict

							def  lowercase							(self						)  ->     Tuple:
														_snake_case     =						self.prepare_config_and_inputs()
														return config, inputs_dict

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     Optional[Any]:
														_snake_case     =						20
														_snake_case     =						model_class_name(lowercase_						)

														_snake_case     =						inputs_dict['''input_ids''']
														_snake_case     =						inputs_dict['''attention_mask''']

														_snake_case     =						model.init_cache(input_ids.shape[0]						,						lowercase_						)
														_snake_case     =						jnp.ones((input_ids.shape[0], max_length)						,						dtype="""i4"""						)

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

														_snake_case     =						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,						dtype="""i4"""						)
														_snake_case     =						model(
														    input_ids[:, -1:]						,						attention_mask=lowercase_						,						past_key_values=outputs_cache.past_key_values						,						position_ids=lowercase_						,						)

														_snake_case     =						model(lowercase_						)

														_snake_case     =						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  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     Tuple:
														_snake_case     =						20
														_snake_case     =						model_class_name(lowercase_						)

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

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

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

														_snake_case     =						model(
														    input_ids[:, :-1]						,						attention_mask=lowercase_						,						past_key_values=lowercase_						,						position_ids=lowercase_						,						)
														_snake_case     =						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,						dtype="""i4"""						)
														_snake_case     =						model(
														    input_ids[:, -1:]						,						past_key_values=outputs_cache.past_key_values						,						attention_mask=lowercase_						,						position_ids=lowercase_						,						)

														_snake_case     =						model(lowercase_						,						attention_mask=lowercase_						)

														_snake_case     =						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					(					__UpperCAmelCase  ,							unittest.TestCase  ,							__UpperCAmelCase   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
							lowerCAmelCase_      		=		() if is_flax_available() else ()

							def  lowercase							(self						)  ->     Optional[Any]:
														_snake_case     =						FlaxOPTModelTester(self						)

							def  lowercase							(self						)  ->     Dict:
														_snake_case     =						self.model_tester.prepare_config_and_inputs()
														for model_class in self.all_model_classes:
																					self.model_tester.check_use_cache_forward(lowercase_						,						lowercase_						,						lowercase_						)

							def  lowercase							(self						)  ->     Union[str, Any]:
														_snake_case     =						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(lowercase_						,						lowercase_						,						lowercase_						)





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

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


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







							'''simple docstring'''

							@slow
							def  lowercase							(self						)  ->     List[str]:
														_snake_case     =						FlaxOPTModel.from_pretrained("""facebook/opt-350m"""						)
														_snake_case     =						jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]						)
														_snake_case     =						model(input_ids=lowercase_						).last_hidden_state
														_snake_case     =						(1, 11, 512)
														self.assertEqual(output.shape						,						lowercase_						)
														_snake_case     =						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]						,						lowercase_						,						atol=4e-2						)						)


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







							'''simple docstring'''

							def  lowercase							(self						)  ->     Union[str, Any]:
														super().setUp()
														_snake_case     =						'''facebook/opt-350m'''





							def  lowercase							(self						)  ->     Union[str, Any]:
														_snake_case     =						FlaxOPTForCausalLM.from_pretrained(self.path_model						)
														_snake_case     =						GPTaTokenizer.from_pretrained(self.path_model						)

														_snake_case     =						[
														    '''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
														_snake_case     =						tokenizer(lowercase_						,						return_tensors="""jax"""						,						padding=lowercase_						,						add_special_tokens=lowercase_						)
														_snake_case     =						model(inputs.input_ids						,						attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
														_snake_case     =						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(lowercase_						,						lowercase_						,						atol=4e-2						)						)

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


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







							'''simple docstring'''

							@property
							def  lowercase							(self						)  ->     Optional[Any]:
														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  lowercase							(self						)  ->     Any:
														_snake_case     =						'''facebook/opt-125m'''

														_snake_case     =						[
														    '''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''',
														]

														_snake_case     =						[]

														_snake_case     =						FlaxOPTForCausalLM.from_pretrained(lowercase_						)
														_snake_case     =						GPTaTokenizer.from_pretrained(lowercase_						)

														for prompt in self.prompts:
																					_snake_case     =						tokenizer(lowercase_						,						return_tensors="""jax"""						).input_ids

																					_snake_case     =						model.generate(lowercase_						,						max_length=10						)
																					_snake_case     =						generated_ids[0]

																					_snake_case     =						tokenizer.batch_decode(lowercase_						,						skip_special_tokens=lowercase_						)
																					predicted_outputs += generated_string

														self.assertListEqual(lowercase_						,						lowercase_						)

							def  lowercase							(self						)  ->     int:
														_snake_case     =						'''facebook/opt-350m'''

														_snake_case     =						[
														    '''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''',
														]

														_snake_case     =						[]
														_snake_case     =						FlaxOPTForCausalLM.from_pretrained(lowercase_						)
														_snake_case     =						GPTaTokenizer.from_pretrained(lowercase_						)

														for prompt in self.prompts:
																					_snake_case     =						tokenizer(lowercase_						,						return_tensors="""jax"""						).input_ids

																					_snake_case     =						model.generate(lowercase_						,						max_length=10						)
																					_snake_case     =						generated_ids[0]

																					_snake_case     =						tokenizer.batch_decode(lowercase_						,						skip_special_tokens=lowercase_						)
																					predicted_outputs += generated_string

														self.assertListEqual(lowercase_						,						lowercase_						)

							def  lowercase							(self						)  ->     int:
														_snake_case     =						'''facebook/opt-125m'''
														_snake_case     =						[
														    '''Today is a beautiful day and I want to thank''',
														    '''In the city of Rome Canaver Canaver Canaver Canaver''',
														]
														_snake_case     =						FlaxOPTForCausalLM.from_pretrained(lowercase_						)
														_snake_case     =						GPTaTokenizer.from_pretrained(lowercase_						)
														_snake_case     =						tokenizer(
														    [
														        """Today is a beautiful day and I want to""",
														        """In the city of""",
														    ]						,						return_tensors="""jax"""						,						padding=lowercase_						,						)

														_snake_case     =						jax.jit(model.generate						)

														_snake_case     =						jit_generate(inputs["""input_ids"""]						,						attention_mask=inputs["""attention_mask"""]						).sequences

														_snake_case     =						tokenizer.batch_decode(lowercase_						,						skip_special_tokens=lowercase_						)

														self.assertIsNotNone(lowercase_						,						lowercase_						)





							def  lowercase							(self						)  ->     Tuple:
														_snake_case     =						'''facebook/opt-350m'''

														_snake_case     =						GPTaTokenizer.from_pretrained(lowercase_						)
														_snake_case     =						FlaxOPTForCausalLM.from_pretrained(lowercase_						)

														_snake_case     =						'''left'''

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

														_snake_case     =						tokenizer(lowercase_						,						return_tensors="""jax"""						,						padding=lowercase_						)
														_snake_case     =						inputs['''input_ids''']

														_snake_case     =						model.generate(input_ids=lowercase_						,						attention_mask=inputs["""attention_mask"""]						,						trace=lowercase_						)

														_snake_case     =						tokenizer(sentences[0]						,						return_tensors="""jax"""						).input_ids
														_snake_case     =						model.generate(input_ids=lowercase_						)

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

														_snake_case     =						tokenizer.batch_decode(outputs[0]						,						skip_special_tokens=lowercase_						)
														_snake_case     =						tokenizer.decode(output_non_padded[0][0]						,						skip_special_tokens=lowercase_						)
														_snake_case     =						tokenizer.decode(output_padded[0][0]						,						skip_special_tokens=lowercase_						)

														_snake_case     =						[
														    '''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(lowercase_						,						lowercase_						)
														self.assertListEqual(lowercase_						,						[non_padded_sentence, padded_sentence]						)

'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase  							= {
    'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase  							= ['DetaImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase  							= [
				    'DETA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DetaForObjectDetection',
				    'DetaModel',
				    'DetaPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_deta import DetaImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deta import (
								    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetaForObjectDetection,
								    DetaModel,
								    DetaPreTrainedModel,
								)

else:
				import sys

				__lowerCAmelCase  							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)