
import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
		import os

		import jax.numpy as jnp
		from jax import jit

		from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
		from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

		lowercase  : Union[str, Any]						   =							"""0.12"""  # assumed parallelism: 8


if is_torch_available():
		import torch







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=None    )			->   List[str]:
							if rng is None:
														lowercase							:					Any	     =				random.Random()

							lowercase							:					Any	     =				1
							for dim in shape:
														total_dims *= dim

							lowercase							:					Optional[Any]	     =				[]
							for _ in range(SCREAMING_SNAKE_CASE__    ):
														values.append(rng.randint(0 ,     vocab_size - 1    )    )

							lowercase							:					str	     =				np.array(SCREAMING_SNAKE_CASE__ ,     dtype=jnp.intaa    ).reshape(SCREAMING_SNAKE_CASE__    )

							return output







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=None    )			->   List[str]:
							lowercase							:					Any	     =				ids_tensor(SCREAMING_SNAKE_CASE__ ,     vocab_size=2 ,     rng=SCREAMING_SNAKE_CASE__    )
							# make sure that at least one token is attended to for each batch
							lowercase							:					List[str]	     =				1
							return attn_mask

@require_flax
class __snake_case		:
				_a		:						Optional[Any]=    None
				_a		:						Any=    ()
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					Optional[int]	     =				self.model_tester.prepare_config_and_inputs_for_common()

											# cut to half length & take max batch_size 3
											lowercase							:					str	     =				2
											lowercase							:					Dict	     =				inputs["""input_ids"""].shape[-1] // 2
											lowercase							:					str	     =				inputs["""input_ids"""][:max_batch_size, :sequence_length]

											lowercase							:					Optional[Any]	     =				jnp.ones_like(snake_case				)
											lowercase							:					Tuple	     =				attention_mask[:max_batch_size, :sequence_length]

											# generate max 5 tokens
											lowercase							:					List[str]	     =				input_ids.shape[-1] + 5
											if config.eos_token_id is not None and config.pad_token_id is None:
																		# hack to allow generate for models such as GPT2 as is done in `generate()`
																		lowercase							:					Union[str, Any]	     =				config.eos_token_id
											return config, input_ids, attention_mask, max_length
				@is_pt_flax_cross_test
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					Optional[int]	     =				self._get_input_ids_and_config()
											lowercase							:					Optional[int]	     =				False
											lowercase							:					int	     =				max_length
											lowercase							:					Union[str, Any]	     =				0

											for model_class in self.all_generative_model_classes:
																		lowercase							:					Dict	     =				model_class(snake_case				)

																		lowercase							:					Any	     =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
																		lowercase							:					Optional[int]	     =				getattr(snake_case		,snake_case				)
																		lowercase							:					Optional[int]	     =				pt_model_class(snake_case				).eval()
																		lowercase							:					Optional[int]	     =				load_flax_weights_in_pytorch_model(snake_case		,flax_model.params				)

																		lowercase							:					Any	     =				flax_model.generate(snake_case				).sequences
																		lowercase							:					Optional[Any]	     =				pt_model.generate(torch.tensor(snake_case		,dtype=torch.long				)				)

																		if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																									lowercase							:					Dict	     =				flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

																		self.assertListEqual(pt_generation_outputs.numpy().tolist()		,flax_generation_outputs.tolist()				)
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					Optional[int]	     =				self._get_input_ids_and_config()
											lowercase							:					int	     =				False
											lowercase							:					str	     =				max_length

											for model_class in self.all_generative_model_classes:
																		lowercase							:					Optional[Any]	     =				model_class(snake_case				)

																		lowercase							:					str	     =				model.generate(snake_case				).sequences
																		self.assertEqual(generation_outputs.shape[-1]		,snake_case				)

																		lowercase							:					str	     =				jit(model.generate				)
																		lowercase							:					Tuple	     =				jit_generate(snake_case				).sequences

																		self.assertListEqual(generation_outputs.tolist()		,jit_generation_outputs.tolist()				)
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					Tuple	     =				self._get_input_ids_and_config()
											lowercase							:					List[Any]	     =				True
											lowercase							:					Optional[Any]	     =				max_length

											for model_class in self.all_generative_model_classes:
																		lowercase							:					Dict	     =				model_class(snake_case				)

																		lowercase							:					Tuple	     =				model.generate(snake_case				).sequences
																		self.assertEqual(generation_outputs.shape[-1]		,snake_case				)

																		lowercase							:					List[str]	     =				jit(model.generate				)
																		lowercase							:					Any	     =				jit_generate(snake_case				).sequences

																		self.assertListEqual(generation_outputs.tolist()		,jit_generation_outputs.tolist()				)
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					List[str]	     =				self._get_input_ids_and_config()
											lowercase							:					Optional[Any]	     =				False
											lowercase							:					int	     =				max_length
											lowercase							:					str	     =				2

											for model_class in self.all_generative_model_classes:
																		lowercase							:					str	     =				model_class(snake_case				)

																		lowercase							:					List[str]	     =				model.generate(snake_case				).sequences
																		self.assertEqual(generation_outputs.shape[-1]		,snake_case				)

																		lowercase							:					List[Any]	     =				jit(model.generate				)
																		lowercase							:					List[str]	     =				jit_generate(snake_case				).sequences

																		self.assertListEqual(generation_outputs.tolist()		,jit_generation_outputs.tolist()				)
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					int	     =				self._get_input_ids_and_config()
											lowercase							:					str	     =				False
											lowercase							:					int	     =				max_length
											lowercase							:					str	     =				2
											lowercase							:					int	     =				2

											for model_class in self.all_generative_model_classes:
																		lowercase							:					Any	     =				model_class(snake_case				)

																		lowercase							:					Any	     =				model.generate(snake_case				).sequences
																		self.assertEqual(generation_outputs.shape[0]		,input_ids.shape[0] * config.num_return_sequences				)
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					Any	     =				self._get_input_ids_and_config()
											lowercase							:					List[Any]	     =				True
											lowercase							:					str	     =				max_length
											lowercase							:					List[str]	     =				0.8
											lowercase							:					Optional[int]	     =				10
											lowercase							:					List[str]	     =				0.3
											lowercase							:					List[Any]	     =				1
											lowercase							:					Dict	     =				8
											lowercase							:					Optional[int]	     =				9

											for model_class in self.all_generative_model_classes:
																		lowercase							:					List[Any]	     =				model_class(snake_case				)

																		lowercase							:					Any	     =				model.generate(snake_case				).sequences
																		self.assertEqual(generation_outputs.shape[-1]		,snake_case				)

																		lowercase							:					Tuple	     =				jit(model.generate				)
																		lowercase							:					Any	     =				jit_generate(snake_case				).sequences

																		self.assertListEqual(generation_outputs.tolist()		,jit_generation_outputs.tolist()				)
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					List[str]	     =				self._get_input_ids_and_config()
											lowercase							:					List[str]	     =				max_length
											lowercase							:					Dict	     =				1
											lowercase							:					Any	     =				8
											lowercase							:					Union[str, Any]	     =				9

											for model_class in self.all_generative_model_classes:
																		lowercase							:					List[str]	     =				model_class(snake_case				)

																		lowercase							:					List[str]	     =				model.generate(snake_case				).sequences
																		self.assertEqual(generation_outputs.shape[-1]		,snake_case				)

																		lowercase							:					Optional[Any]	     =				jit(model.generate				)
																		lowercase							:					str	     =				jit_generate(snake_case				).sequences

																		self.assertListEqual(generation_outputs.tolist()		,jit_generation_outputs.tolist()				)
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					Union[str, Any]	     =				self._get_input_ids_and_config()
											lowercase							:					Any	     =				max_length
											lowercase							:					List[str]	     =				2
											lowercase							:					str	     =				1
											lowercase							:					Dict	     =				8
											lowercase							:					Any	     =				9

											for model_class in self.all_generative_model_classes:
																		lowercase							:					int	     =				model_class(snake_case				)

																		lowercase							:					Union[str, Any]	     =				model.generate(snake_case				).sequences
																		self.assertEqual(generation_outputs.shape[-1]		,snake_case				)

																		lowercase							:					str	     =				jit(model.generate				)
																		lowercase							:					str	     =				jit_generate(snake_case				).sequences

																		self.assertListEqual(generation_outputs.tolist()		,jit_generation_outputs.tolist()				)
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					Optional[Any]	     =				self._get_input_ids_and_config()

											# pad attention mask on the left
											lowercase							:					Any	     =				attention_mask.at[(0, 0)].set(0				)

											lowercase							:					Tuple	     =				False
											lowercase							:					List[str]	     =				max_length

											for model_class in self.all_generative_model_classes:
																		lowercase							:					Optional[Any]	     =				model_class(snake_case				)

																		lowercase							:					Tuple	     =				model.generate(snake_case		,attention_mask=snake_case				).sequences
																		self.assertEqual(generation_outputs.shape[-1]		,snake_case				)

																		lowercase							:					Optional[int]	     =				jit(model.generate				)
																		lowercase							:					Optional[int]	     =				jit_generate(snake_case		,attention_mask=snake_case				).sequences

																		self.assertListEqual(generation_outputs.tolist()		,jit_generation_outputs.tolist()				)
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					List[Any]	     =				self._get_input_ids_and_config()

											# pad attention mask on the left
											lowercase							:					Optional[Any]	     =				attention_mask.at[(0, 0)].set(0				)

											lowercase							:					Any	     =				True
											lowercase							:					Dict	     =				max_length

											for model_class in self.all_generative_model_classes:
																		lowercase							:					List[str]	     =				model_class(snake_case				)

																		lowercase							:					Tuple	     =				model.generate(snake_case		,attention_mask=snake_case				).sequences
																		self.assertEqual(generation_outputs.shape[-1]		,snake_case				)

																		lowercase							:					str	     =				jit(model.generate				)
																		lowercase							:					Optional[int]	     =				jit_generate(snake_case		,attention_mask=snake_case				).sequences

																		self.assertListEqual(generation_outputs.tolist()		,jit_generation_outputs.tolist()				)
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					List[Any]	     =				self._get_input_ids_and_config()

											# pad attention mask on the left
											lowercase							:					List[str]	     =				attention_mask.at[(0, 0)].set(0				)

											lowercase							:					Dict	     =				2
											lowercase							:					Dict	     =				max_length

											for model_class in self.all_generative_model_classes:
																		lowercase							:					Dict	     =				model_class(snake_case				)

																		lowercase							:					Dict	     =				model.generate(snake_case		,attention_mask=snake_case				).sequences
																		self.assertEqual(generation_outputs.shape[-1]		,snake_case				)

																		lowercase							:					int	     =				jit(model.generate				)
																		lowercase							:					Optional[int]	     =				jit_generate(snake_case		,attention_mask=snake_case				).sequences

																		self.assertListEqual(generation_outputs.tolist()		,jit_generation_outputs.tolist()				)






@require_flax
class __snake_case		(				unittest.TestCase					):
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					str	     =				AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""				)
											lowercase							:					str	     =				FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""				)

											lowercase							:					Optional[int]	     =				"""Hello world"""
											lowercase							:					Tuple	     =				tokenizer(snake_case		,return_tensors="""np"""				).input_ids

											# typos are quickly detected (the correct argument is `do_sample`)
											with self.assertRaisesRegex(snake_case		,"""do_samples"""				):
																		model.generate(snake_case		,do_samples=snake_case				)

											# arbitrary arguments that will not be used anywhere are also not accepted
											with self.assertRaisesRegex(snake_case		,"""foo"""				):
																		lowercase							:					Any	     =				{"""foo""": """bar"""}
																		model.generate(snake_case		,**snake_case				)


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase  : List[Any]						   =							logging.get_logger(__name__)

lowercase  : List[Any]						   =							{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}

class __snake_case		(				lowerCAmelCase					):
    _a		:						Dict=    "mobilenet_v1"
    def __init__(  self		,snake_case=3		,snake_case=224		,snake_case=1.0		,snake_case=8		,snake_case="relu6"		,snake_case=True		,snake_case=0.999		,snake_case=0.02		,snake_case=0.001		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case				)

           if depth_multiplier <= 0:
                  raise ValueError("""depth_multiplier must be greater than zero."""				)

           lowercase							:					int	     =				num_channels
           lowercase							:					Union[str, Any]	     =				image_size
           lowercase							:					int	     =				depth_multiplier
           lowercase							:					Tuple	     =				min_depth
           lowercase							:					Dict	     =				hidden_act
           lowercase							:					Dict	     =				tf_padding
           lowercase							:					Dict	     =				classifier_dropout_prob
           lowercase							:					int	     =				initializer_range
           lowercase							:					List[str]	     =				layer_norm_eps






class __snake_case		(				lowerCAmelCase					):
    _a		:						int=    version.parse("1.11"					)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return OrderedDict([("""pixel_values""", {0: """batch"""})]				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.task == "image-classification":
                  return OrderedDict([("""logits""", {0: """batch"""})]				)
           else:
                  return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 1e-4
