
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

   _A						      =				"0.12"  # assumed parallelism: 8


if is_torch_available():
   import torch



def 		lowercase_    (     A__				,	A__				,	A__=None	)							->							Optional[int]:
    """simple docstring"""

    if rng is None:
        snake_case				  =      random.Random()

    snake_case				  =      1
    for dim in shape:
        total_dims *= dim

    snake_case				  =      []
    for _ in range(A__	):
        values.append(rng.randint(0				,	vocab_size - 1	)	)

    snake_case				  =      np.array(A__				,	dtype=jnp.intaa	).reshape(A__	)

    return output



def 		lowercase_    (     A__				,	A__=None	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      ids_tensor(A__				,	vocab_size=2				,	rng=A__	)
    # make sure that at least one token is attended to for each batch
    snake_case				  =      1
    return attn_mask







@require_flax
class   lowerCamelCase	:
     UpperCAmelCase__       :							Union[str, Any]     =	None
     UpperCAmelCase__       :							List[str]     =	()





     def 	UpperCAmelCase(self   :					int     )		->		Union[str, Any]:
         snake_case  ,							snake_case				  =      self.model_tester.prepare_config_and_inputs_for_common()

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

         snake_case				  =      jnp.ones_like(_A     )
         snake_case				  =      attention_mask[:max_batch_size, :sequence_length]

         # generate max 5 tokens
         snake_case				  =      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()`
             snake_case				  =      config.eos_token_id
         return config, input_ids, attention_mask, max_length





     @is_pt_flax_cross_test
     def 	UpperCAmelCase(self   :					str     )		->		List[Any]:
         snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      self._get_input_ids_and_config()
         snake_case				  =      False
         snake_case				  =      max_length
         snake_case				  =      0

         for model_class in self.all_generative_model_classes:
             snake_case				  =      model_class(_A     )

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

             snake_case				  =      flax_model.generate(_A     ).sequences
             snake_case				  =      pt_model.generate(torch.tensor(_A       ,					dtype=torch.long     )     )

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

             self.assertListEqual(pt_generation_outputs.numpy().tolist()       ,					flax_generation_outputs.tolist()     )





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Optional[int]:
         snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      self._get_input_ids_and_config()
         snake_case				  =      False
         snake_case				  =      max_length

         for model_class in self.all_generative_model_classes:
             snake_case				  =      model_class(_A     )

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

             snake_case				  =      jit(model.generate     )
             snake_case				  =      jit_generate(_A     ).sequences

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





     def 	UpperCAmelCase(self   :					Dict     )		->		Union[str, Any]:
         snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      self._get_input_ids_and_config()
         snake_case				  =      True
         snake_case				  =      max_length

         for model_class in self.all_generative_model_classes:
             snake_case				  =      model_class(_A     )

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

             snake_case				  =      jit(model.generate     )
             snake_case				  =      jit_generate(_A     ).sequences

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





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Any:
         snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      self._get_input_ids_and_config()
         snake_case				  =      False
         snake_case				  =      max_length
         snake_case				  =      2

         for model_class in self.all_generative_model_classes:
             snake_case				  =      model_class(_A     )

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

             snake_case				  =      jit(model.generate     )
             snake_case				  =      jit_generate(_A     ).sequences

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





     def 	UpperCAmelCase(self   :					Dict     )		->		Tuple:
         snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      self._get_input_ids_and_config()
         snake_case				  =      False
         snake_case				  =      max_length
         snake_case				  =      2
         snake_case				  =      2

         for model_class in self.all_generative_model_classes:
             snake_case				  =      model_class(_A     )

             snake_case				  =      model.generate(_A     ).sequences
             self.assertEqual(generation_outputs.shape[0]       ,					input_ids.shape[0] * config.num_return_sequences     )





     def 	UpperCAmelCase(self   :					Any     )		->		Tuple:
         snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      self._get_input_ids_and_config()
         snake_case				  =      True
         snake_case				  =      max_length
         snake_case				  =      0.8
         snake_case				  =      1_0
         snake_case				  =      0.3
         snake_case				  =      1
         snake_case				  =      8
         snake_case				  =      9

         for model_class in self.all_generative_model_classes:
             snake_case				  =      model_class(_A     )

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

             snake_case				  =      jit(model.generate     )
             snake_case				  =      jit_generate(_A     ).sequences

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





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		List[str]:
         snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      self._get_input_ids_and_config()
         snake_case				  =      max_length
         snake_case				  =      1
         snake_case				  =      8
         snake_case				  =      9

         for model_class in self.all_generative_model_classes:
             snake_case				  =      model_class(_A     )

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

             snake_case				  =      jit(model.generate     )
             snake_case				  =      jit_generate(_A     ).sequences

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





     def 	UpperCAmelCase(self   :					Dict     )		->		int:
         snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      self._get_input_ids_and_config()
         snake_case				  =      max_length
         snake_case				  =      2
         snake_case				  =      1
         snake_case				  =      8
         snake_case				  =      9

         for model_class in self.all_generative_model_classes:
             snake_case				  =      model_class(_A     )

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

             snake_case				  =      jit(model.generate     )
             snake_case				  =      jit_generate(_A     ).sequences

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





     def 	UpperCAmelCase(self   :					Dict     )		->		Tuple:
         snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      self._get_input_ids_and_config()

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

         snake_case				  =      False
         snake_case				  =      max_length

         for model_class in self.all_generative_model_classes:
             snake_case				  =      model_class(_A     )

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

             snake_case				  =      jit(model.generate     )
             snake_case				  =      jit_generate(_A       ,					attention_mask=_A     ).sequences

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





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Union[str, Any]:
         snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      self._get_input_ids_and_config()

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

         snake_case				  =      True
         snake_case				  =      max_length

         for model_class in self.all_generative_model_classes:
             snake_case				  =      model_class(_A     )

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

             snake_case				  =      jit(model.generate     )
             snake_case				  =      jit_generate(_A       ,					attention_mask=_A     ).sequences

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





     def 	UpperCAmelCase(self   :					Optional[int]     )		->		Any:
         snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      self._get_input_ids_and_config()

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

         snake_case				  =      2
         snake_case				  =      max_length

         for model_class in self.all_generative_model_classes:
             snake_case				  =      model_class(_A     )

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

             snake_case				  =      jit(model.generate     )
             snake_case				  =      jit_generate(_A       ,					attention_mask=_A     ).sequences

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







@require_flax
class   lowerCamelCase	(						unittest.TestCase						):





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		int:
         snake_case				  =      AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"     )
         snake_case				  =      FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"     )

         snake_case				  =      "Hello world"
         snake_case				  =      tokenizer(_A       ,					return_tensors="np"     ).input_ids

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

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



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import MobileNetVaImageProcessor







class   lowerCamelCase	(						unittest.TestCase						):





     def __init__(self   :					Optional[int]       ,					_A   :					Dict       ,					_A   :					Optional[int]=7       ,					_A   :					Union[str, Any]=3       ,					_A   :					Tuple=1_8       ,					_A   :					str=3_0       ,					_A   :					Union[str, Any]=4_0_0       ,					_A   :					Optional[Any]=True       ,					_A   :					Union[str, Any]=None       ,					_A   :					str=True       ,					_A   :					Optional[int]=None       ,					)		->		int:
         snake_case				  =      size if size is not None else {"shortest_edge": 2_0}
         snake_case				  =      crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
         snake_case				  =      parent
         snake_case				  =      batch_size
         snake_case				  =      num_channels
         snake_case				  =      image_size
         snake_case				  =      min_resolution
         snake_case				  =      max_resolution
         snake_case				  =      do_resize
         snake_case				  =      size
         snake_case				  =      do_center_crop
         snake_case				  =      crop_size





     def 	UpperCAmelCase(self   :					str     )		->		int:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }







@require_torch
@require_vision
class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							Optional[int]     =	MobileNetVaImageProcessor if is_vision_available() else None





     def 	UpperCAmelCase(self   :					Dict     )		->		List[Any]:
         snake_case				  =      MobileNetVaImageProcessingTester(self     )





     @property
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Tuple:
         return self.image_processor_tester.prepare_image_processor_dict()





     def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         self.assertTrue(hasattr(_A       ,					"do_resize"     )     )
         self.assertTrue(hasattr(_A       ,					"size"     )     )
         self.assertTrue(hasattr(_A       ,					"do_center_crop"     )     )
         self.assertTrue(hasattr(_A       ,					"crop_size"     )     )





     def 	UpperCAmelCase(self   :					int     )		->		List[Any]:
         snake_case				  =      self.image_processing_class.from_dict(self.image_processor_dict     )
         self.assertEqual(image_processor.size       ,					{"shortest_edge": 2_0}     )
         self.assertEqual(image_processor.crop_size       ,					{"height": 1_8, "width": 1_8}     )

         snake_case				  =      self.image_processing_class.from_dict(self.image_processor_dict       ,					size=4_2       ,					crop_size=8_4     )
         self.assertEqual(image_processor.size       ,					{"shortest_edge": 4_2}     )
         self.assertEqual(image_processor.crop_size       ,					{"height": 8_4, "width": 8_4}     )





     def 	UpperCAmelCase(self   :					Any     )		->		Tuple:
         pass





     def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         snake_case				  =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					Image.Image     )

         # Test not batched input
         snake_case				  =      image_processing(image_inputs[0]       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)

         # Test batched
         snake_case				  =      image_processing(_A       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)





     def 	UpperCAmelCase(self   :					Tuple     )		->		List[Any]:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random numpy tensors
         snake_case				  =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_A       ,					numpify=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					np.ndarray     )

         # Test not batched input
         snake_case				  =      image_processing(image_inputs[0]       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)

         # Test batched
         snake_case				  =      image_processing(_A       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)





     def 	UpperCAmelCase(self   :					List[str]     )		->		List[str]:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random PyTorch tensors
         snake_case				  =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_A       ,					torchify=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					torch.Tensor     )

         # Test not batched input
         snake_case				  =      image_processing(image_inputs[0]       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)

         # Test batched
         snake_case				  =      image_processing(_A       ,					return_tensors="pt"     ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)

