

import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
       import jax

       from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel






class  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  def __init__(							self		:	Optional[int]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	List[str]=13			,     __lowerCamelCase		:	Optional[Any]=30			,     __lowerCamelCase		:	Dict=2			,     __lowerCamelCase		:	List[Any]=3			,     __lowerCamelCase		:	List[str]=True			,     __lowerCamelCase		:	str=True			,     __lowerCamelCase		:	Optional[int]=32			,     __lowerCamelCase		:	Dict=5			,     __lowerCamelCase		:	Optional[int]=4			,     __lowerCamelCase		:	List[Any]=37			,     __lowerCamelCase		:	Union[str, Any]="gelu"			,     __lowerCamelCase		:	List[Any]=0.1			,     __lowerCamelCase		:	Any=0.1			,     __lowerCamelCase		:	str=10			,     __lowerCamelCase		:	Dict=0.02			,     )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					parent
       SCREAMING_SNAKE_CASE__					=					batch_size
       SCREAMING_SNAKE_CASE__					=					image_size
       SCREAMING_SNAKE_CASE__					=					patch_size
       SCREAMING_SNAKE_CASE__					=					num_channels
       SCREAMING_SNAKE_CASE__					=					is_training
       SCREAMING_SNAKE_CASE__					=					use_labels
       SCREAMING_SNAKE_CASE__					=					hidden_size
       SCREAMING_SNAKE_CASE__					=					num_hidden_layers
       SCREAMING_SNAKE_CASE__					=					num_attention_heads
       SCREAMING_SNAKE_CASE__					=					intermediate_size
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					=					attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					=					type_sequence_label_size
       SCREAMING_SNAKE_CASE__					=					initializer_range

       # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       SCREAMING_SNAKE_CASE__					=					(image_size // patch_size) ** 2
       SCREAMING_SNAKE_CASE__					=					num_patches + 1






  def    lowercase_	(							self		:	List[Any]      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       SCREAMING_SNAKE_CASE__					=					ViTConfig(
           image_size=self.image_size			,     patch_size=self.patch_size			,     num_channels=self.num_channels			,     hidden_size=self.hidden_size			,     num_hidden_layers=self.num_hidden_layers			,     num_attention_heads=self.num_attention_heads			,     intermediate_size=self.intermediate_size			,     hidden_act=self.hidden_act			,     hidden_dropout_prob=self.hidden_dropout_prob			,     attention_probs_dropout_prob=self.attention_probs_dropout_prob			,     is_decoder=__lowerCamelCase			,     initializer_range=self.initializer_range			,     )

       return config, pixel_values






  def    lowercase_	(							self		:	Tuple			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	Optional[Any]      )	->							int:
       SCREAMING_SNAKE_CASE__					=					FlaxViTModel(config=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )
       # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
       SCREAMING_SNAKE_CASE__					=					(self.image_size, self.image_size)
       SCREAMING_SNAKE_CASE__					=					(self.patch_size, self.patch_size)
       SCREAMING_SNAKE_CASE__					=					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
       self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, num_patches + 1, self.hidden_size)      )






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Dict      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					self.type_sequence_label_size
       SCREAMING_SNAKE_CASE__					=					FlaxViTForImageClassification(config=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )
       self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       SCREAMING_SNAKE_CASE__					=					1
       SCREAMING_SNAKE_CASE__					=					FlaxViTForImageClassification(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )







  def    lowercase_	(							self		:	int      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE__     
       ),(
           SCREAMING_SNAKE_CASE__     
       ),     
       )					=					config_and_inputs
       SCREAMING_SNAKE_CASE__					=					{'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_flax
class  UpperCAmelCase__	( _snake_case		,  unittest.TestCase ):






  """simple docstring"""


  a     =      (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






  def    lowercase_	(							self		:	Union[str, Any]      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					FlaxViTModelTester(self      )
       SCREAMING_SNAKE_CASE__					=					ConfigTester(self			,     config_class=__lowerCamelCase			,     has_text_modality=__lowerCamelCase			,     hidden_size=37      )






  def    lowercase_	(							self		:	Tuple      )	->							Optional[int]:
       self.config_tester.run_common_tests()






  def    lowercase_	(							self		:	List[str]      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase      )






  def    lowercase_	(							self		:	List[Any]      )	->							str:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase      )






  def    lowercase_	(							self		:	List[Any]      )	->							str:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__					=					model_class(__lowerCamelCase      )
            SCREAMING_SNAKE_CASE__					=					inspect.signature(model.__call__      )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            SCREAMING_SNAKE_CASE__					=					[*signature.parameters.keys()]

            SCREAMING_SNAKE_CASE__					=					['''pixel_values''']
            self.assertListEqual(arg_names[:1]			,     __lowerCamelCase      )






  def    lowercase_	(							self		:	Optional[int]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__      ):
                 SCREAMING_SNAKE_CASE__					=					self._prepare_for_class(__lowerCamelCase			,     __lowerCamelCase      )
                 SCREAMING_SNAKE_CASE__					=					model_class(__lowerCamelCase      )

                 @jax.jit
                 def model_jitted(__lowerCamelCase		:	int			,     **__lowerCamelCase		:	Union[str, Any]      ):
                      return model(pixel_values=__lowerCamelCase			,     **__lowerCamelCase      )

                 with self.subTest('''JIT Enabled'''      ):
                      SCREAMING_SNAKE_CASE__					=					model_jitted(**__lowerCamelCase      ).to_tuple()

                 with self.subTest('''JIT Disabled'''      ):
                      with jax.disable_jit():
                           SCREAMING_SNAKE_CASE__					=					model_jitted(**__lowerCamelCase      ).to_tuple()

                 self.assertEqual(len(__lowerCamelCase      )			,     len(__lowerCamelCase      )      )
                 for jitted_output, output in zip(__lowerCamelCase			,     __lowerCamelCase      ):
                      self.assertEqual(jitted_output.shape			,     output.shape      )







  @slow
  def    lowercase_	(							self		:	Optional[Any]      )	->							List[str]:

       for model_class_name in self.all_model_classes:
            SCREAMING_SNAKE_CASE__					=					model_class_name.from_pretrained('''google/vit-base-patch16-224'''      )
            SCREAMING_SNAKE_CASE__					=					model(np.ones((1, 3, 224, 224)      )      )
            self.assertIsNotNone(__lowerCamelCase      )





from dataclasses import dataclass, field
from typing import Optional






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Model name or path of model to be trained."} )
  a     =      field(
      default="./"		,  metadata={"help": "Save dir where model repo is cloned and models updates are saved to."} )
  a     =      field(
      default="codeparrot/codeparrot-clean-train"		,  metadata={"help": "Name or path of training dataset."} )
  a     =      field(
      default="codeparrot/codeparrot-clean-valid"		,  metadata={"help": "Name or path of validation dataset."} )
  a     =      field(default=2		,  metadata={"help": "Batch size for training."} )
  a     =      field(default=2		,  metadata={"help": "Batch size for evaluation."} )
  a     =      field(default=0.1		,  metadata={"help": "Value of weight decay."} )
  a     =      field(
      default=1_00_00		,  metadata={"help": "Size of buffer used to shuffle streaming dataset."} )
  a     =      field(default=2e-4		,  metadata={"help": "Learning rate fo training."} )
  a     =      field(default="cosine"		,  metadata={"help": "Learning rate."} )
  a     =      field(
      default=7_50		,  metadata={"help": "Number of warmup steps in the learning rate schedule."} )
  a     =      field(
      default=16		,  metadata={"help": "Number of gradient accumulation steps."} )
  a     =      field(
      default=A__		,  metadata={"help": "Use gradient checkpointing to reduce memory footprint."} )
  a     =      field(default=5_00_00		,  metadata={"help": "Maximum number of training steps."} )
  a     =      field(
      default=-1		,  metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."} )
  a     =      field(default=10_24		,  metadata={"help": "Sequence lengths used for training."} )
  a     =      field(default=1		,  metadata={"help": "Training seed."} )
  a     =      field(
      default=10_24		,  metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "States path if the training should continue from a checkpoint folder."} )
  a     =      field(default=A__		,  metadata={"help": "If True the data is pretokenized."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Model name or path of model to be evaluated."} )
  a     =      field(
      default="codeparrot/codeparrot-clean-valid"		,  metadata={"help": "Name or path of validation dataset."} )
  a     =      field(default=2		,  metadata={"help": "Batch size used for evaluation."} )
  a     =      field(
      default=-1		,  metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."} )
  a     =      field(default=10_24		,  metadata={"help": "Length of sequences to be evaluated."} )
  a     =      field(default=1		,  metadata={"help": "Random seed used for evaluation."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Model name or path of model to be evaluated."} )
  a     =      field(default=A__		,  metadata={"help": "Number of workers used for code evaluation."} )
  a     =      field(
      default=A__		,  metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "Sample from the language model's output distribution."} )
  a     =      field(default=0.2		,  metadata={"help": "Sampling temperature used for generation."} )
  a     =      field(default=2_56		,  metadata={"help": "Maximum number of newly generated tokens."} )
  a     =      field(default=0		,  metadata={"help": "Top-k parameter used for generation."} )
  a     =      field(default=0.9_5		,  metadata={"help": "Top-p parameter used for nucleus sampling."} )
  a     =      field(default=10		,  metadata={"help": "Number of generations to run in parallel."} )
  a     =      field(
      default=2_00		,  metadata={"help": "Number of completions to generate for each sample."} )
  a     =      field(default=1		,  metadata={"help": "Random seed used for evaluation."} )
  a     =      field(
      default="eval_results.json"		,  metadata={"help": "Random seed used for evaluation."} )
  a     =      field(
      default="0"		,  metadata={"help": "Allow `code_eval` to execute Python code on machine"} )
  a     =      field(
      default=-1		,  metadata={
          "help": (
              "Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"
              " number corresponds to which GPU device id to run on."
          )
      }		,  )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default=A__		,  metadata={
          "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
      }		,  )
  a     =      field(
      default="transformersbook/codeparrot"		,  metadata={"help": "Folder or name of dataset to process."} )
  a     =      field(
      default="codeparrot-clean"		,  metadata={"help": "Folder to save processed processed dataset."} )
  a     =      field(
      default=10_00_00		,  metadata={"help": "Number of files to save per JSON output file."} )
  a     =      field(default="content"		,  metadata={"help": "Column containing text data to process."} )
  a     =      field(
      default=10_00		,  metadata={"help": "Maximum line length in file, otherwise file is filtered."} )
  a     =      field(
      default=1_00		,  metadata={"help": "Maximum mean line length in file, otherwise file is filtered."} )
  a     =      field(
      default=0.2_5		,  metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."} )
  a     =      field(
      default=1.5		,  metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."} )
  a     =      field(
      default=0.7		,  metadata={"help": "Probability for filtering config, test and uncommon files."} )
  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Name or path to the tokenizer."}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "If True, near-duplicate samples are removed."} )
  a     =      field(
      default=0.8_5		,  metadata={"help": "Jaccard threshold for near-duplicate samples."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="gpt2"		,  metadata={"help": "Base tokenizer to build new tokenizer from."} )
  a     =      field(
      default="transformersbook/codeparrot-train"		,  metadata={"help": "Dataset to train tokenizer on."} )
  a     =      field(default="content"		,  metadata={"help": "Column containing text data to process."} )
  a     =      field(default=20_00_00		,  metadata={"help": "Number of examples to train tokenizer on."} )
  a     =      field(
      default=3_27_68		,  metadata={"help": "Number of examples to train the tokenizer on."} )
  a     =      field(default="codeparrot"		,  metadata={"help": "Name of new tokenizer."} )
  a     =      field(default=A__		,  metadata={"help": "Push saved tokenizer to the hub."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Name or path to the tokenizer."} )
  a     =      field(
      default="codeparrot/codeparrot-clean-train"		,  metadata={"help": "Name or path to the dataset to pretokenize."} )
  a     =      field(
      default="tokenized-codeparrot-train"		,  metadata={"help": "Repo name of the pretokenized data."} )
  a     =      field(default=A__		,  metadata={"help": "Number of workers used for code evaluation."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="gpt2-large"		,  metadata={"help": "Configuration to use for model initialization."} )
  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Tokenizer attached to model."} )
  a     =      field(default="codeparrot"		,  metadata={"help": "Name of the created model."} )
  a     =      field(default=A__		,  metadata={"help": "Push saved tokenizer to the hub."} )


