


from typing import TYPE_CHECKING

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


_lowerCAmelCase:	Union[str, Any]   							=					{
    'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:	Optional[Any]   							=					[
	    '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:	str   							=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





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:	Tuple      							=					'platform'

	import jax
	import jax.numpy as jnp

	from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def      _lowercase(				__a							:	Any						,    __a							:	Any						,    __a							:	Tuple=None						,    __a							:	List[str]=None    ):
							if attention_mask is None:
														a__     						=np.where(input_ids != config.pad_token_id						,    1						,    0    )
							return {
							    "input_ids": input_ids,
							    "attention_mask": attention_mask,
							}




@require_flax
class 			lowercase_				:





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





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

											a__     						=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_					,     )
											a__     						=prepare_opt_inputs_dict(lowercase_					,     lowercase_)
											return config, inputs_dict





				def 	__UpperCamelCase						(							self)     ->   List[Any]:
											a__						,    a__     						=self.prepare_config_and_inputs()
											return config, inputs_dict





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_)     ->   Any:
											a__     						=20
											a__     						=model_class_name(lowercase_)

											a__     						=inputs_dict['input_ids']
											a__     						=inputs_dict['attention_mask']

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

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

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

											a__     						=model(lowercase_)

											a__     						=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					,     lowercase_					,     lowercase_					,     lowercase_)     ->   Optional[Any]:
											a__     						=20
											a__     						=model_class_name(lowercase_)

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

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

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

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

											a__     						=model(lowercase_					,     attention_mask=lowercase_)

											a__     						=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 			lowercase_				(lowercase__			,				unittest.TestCase			,				lowercase__      ):
				snake_case  						=(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
				snake_case  						=() if is_flax_available() else ()





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											a__     						=FlaxOPTModelTester(self)





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											a__						,    a__     						=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 	__UpperCamelCase						(							self)     ->   List[Any]:
											a__						,    a__     						=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 	__UpperCamelCase						(							self)     ->   Tuple:
											for model_class_name in self.all_model_classes:
																		a__     						=model_class_name.from_pretrained('facebook/opt-125m')
																		a__     						=np.ones((1, 1)) * model.config.eos_token_id
																		a__     						=model(lowercase_)
																		self.assertIsNotNone(lowercase_)







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





				@slow
				def 	__UpperCamelCase						(							self)     ->   Union[str, Any]:
											a__     						=FlaxOPTModel.from_pretrained('facebook/opt-350m')
											a__     						=jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
											a__     						=model(input_ids=lowercase_).last_hidden_state
											a__     						=(1, 11, 512)
											self.assertEqual(output.shape					,     lowercase_)
											a__     						=jnp.array(
											    [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]])
											self.assertTrue(jnp.allclose(output[:, :3, :3]					,     lowercase_					,     atol=4e-2))







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





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											super().setUp()
											a__     						='facebook/opt-350m'





				def 	__UpperCamelCase						(							self)     ->   List[Any]:
											a__     						=FlaxOPTForCausalLM.from_pretrained(self.path_model)
											a__     						=GPTaTokenizer.from_pretrained(self.path_model)

											a__     						=[
											    '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
											a__     						=tokenizer(lowercase_					,     return_tensors='jax'					,     padding=lowercase_					,     add_special_tokens=lowercase_)
											a__     						=model(inputs.input_ids					,     attention_mask=inputs.attention_mask)[0].mean(axis=-1)
											a__     						=jnp.array(
											    [
											        [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
											        [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
											        [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
											        [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
											    ])
											self.assertTrue(jnp.allclose(lowercase_					,     lowercase_					,     atol=4e-2))

											a__     						=jax.jit(lowercase_)
											a__     						=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 			lowercase_				(unittest.TestCase      ):





				@property
				def 	__UpperCamelCase						(							self)     ->   List[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 	__UpperCamelCase						(							self)     ->   List[str]:
											a__     						='facebook/opt-125m'

											a__     						=[
											    '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',
											]

											a__     						=[]

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

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

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

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

											self.assertListEqual(lowercase_					,     lowercase_)





				def 	__UpperCamelCase						(							self)     ->   List[Any]:
											a__     						='facebook/opt-350m'

											a__     						=[
											    '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',
											]

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

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

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

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

											self.assertListEqual(lowercase_					,     lowercase_)





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

											a__     						=jax.jit(model.generate)

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

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

											self.assertIsNotNone(lowercase_					,     lowercase_)





				def 	__UpperCamelCase						(							self)     ->   int:
											a__     						='facebook/opt-350m'

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

											a__     						='left'

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

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

											a__     						=model.generate(input_ids=lowercase_					,     attention_mask=inputs['attention_mask']					,     trace=lowercase_)

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

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

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

											a__     						=[
											    '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])

