
import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
				import tensorflow as tf

				tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
				import torch

if is_datasets_available():
				from datasets import load_dataset


lowercase__				:				Dict						=      5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors




def 						A_			(				snake_case				: Namespace			)   ->     List[str]:



						'''simple docstring'''

						return PTtoTFCommand(
						    args.model_name			,   args.local_dir			,   args.max_error			,   args.new_weights			,   args.no_pr			,   args.push			,   args.extra_commit_description			,   args.override_model_class			,   )






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							@staticmethod
							def      A__		(	SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      parser.add_parser(
													    '''pt-to-tf'''  ,						help=(
													        '''CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'''
													        ''' Can also be used to validate existing weights without opening PRs, with --no-pr.'''
													    )  ,						)
													train_parser.add_argument(
													    '''--model-name'''  ,						type=SCREAMING_SNAKE_CASE_  ,						required=SCREAMING_SNAKE_CASE_  ,						help='''The model name, including owner/organization, as seen on the hub.'''  ,						)
													train_parser.add_argument(
													    '''--local-dir'''  ,						type=SCREAMING_SNAKE_CASE_  ,						default=''''''  ,						help='''Optional local directory of the model repository. Defaults to /tmp/{model_name}'''  ,						)
													train_parser.add_argument(
													    '''--max-error'''  ,						type=SCREAMING_SNAKE_CASE_  ,						default=SCREAMING_SNAKE_CASE_  ,						help=(
													        F"Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."
													    )  ,						)
													train_parser.add_argument(
													    '''--new-weights'''  ,						action='''store_true'''  ,						help='''Optional flag to create new TensorFlow weights, even if they already exist.'''  ,						)
													train_parser.add_argument(
													    '''--no-pr'''  ,						action='''store_true'''  ,						help='''Optional flag to NOT open a PR with converted weights.'''      )
													train_parser.add_argument(
													    '''--push'''  ,						action='''store_true'''  ,						help='''Optional flag to push the weights directly to `main` (requires permissions)'''  ,						)
													train_parser.add_argument(
													    '''--extra-commit-description'''  ,						type=SCREAMING_SNAKE_CASE_  ,						default=''''''  ,						help='''Optional additional commit description to use when opening a PR (e.g. to tag the owner).'''  ,						)
													train_parser.add_argument(
													    '''--override-model-class'''  ,						type=SCREAMING_SNAKE_CASE_  ,						default=SCREAMING_SNAKE_CASE_  ,						help='''If you think you know better than the auto-detector, you can specify the model class here. '''
													    '''Can be either an AutoModel class or a specific model class like BertForSequenceClassification.'''  ,						)
													train_parser.set_defaults(func=SCREAMING_SNAKE_CASE_      )



							@staticmethod
							def      A__		(	SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      set(pt_outputs.keys()      )
													__UpperCamelCase  	=      set(tf_outputs.keys()      )
													if pt_out_attrs != tf_out_attrs:
																			raise ValueError(
																			    F"The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"
																			    F" {tf_out_attrs})"      )

													# 2. For each output attribute, computes the difference
													def _find_pt_tf_differences(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=""      ):
																			# If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
																			# recursivelly, keeping the name of the attribute.
																			if isinstance(SCREAMING_SNAKE_CASE_  ,						torch.Tensor      ):
																									__UpperCamelCase  	=      np.max(np.abs(pt_out.numpy() - tf_out.numpy()      )      )
																									__UpperCamelCase  	=      tensor_difference
																			else:
																									__UpperCamelCase  	=      attr_name
																									for i, pt_item in enumerate(SCREAMING_SNAKE_CASE_      ):
																															# If it is a named attribute, we keep the name. Otherwise, just its index.
																															if isinstance(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ):
																																					__UpperCamelCase  	=      root_name + pt_item
																																					__UpperCamelCase  	=      tf_out[pt_item]
																																					__UpperCamelCase  	=      pt_out[pt_item]
																															else:
																																					__UpperCamelCase  	=      root_name + F"[{i}]"
																																					__UpperCamelCase  	=      tf_out[i]
																															__UpperCamelCase  	=      _find_pt_tf_differences(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

																			return differences

													return _find_pt_tf_differences(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						{}      )



							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						*SCREAMING_SNAKE_CASE_  ,						)-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      logging.get_logger('''transformers-cli/pt_to_tf'''      )
													__UpperCamelCase  	=      model_name
													__UpperCamelCase  	=      local_dir if local_dir else os.path.join('''/tmp'''  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      max_error
													__UpperCamelCase  	=      new_weights
													__UpperCamelCase  	=      no_pr
													__UpperCamelCase  	=      push
													__UpperCamelCase  	=      extra_commit_description
													__UpperCamelCase  	=      override_model_class



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> str:





													'''simple docstring'''






													def _get_audio_input():
																			__UpperCamelCase  	=      load_dataset('''hf-internal-testing/librispeech_asr_dummy'''  ,						'''clean'''  ,						split='''validation'''      )
																			__UpperCamelCase  	=      ds.sort('''id'''      ).select(range(2      )      )[:2]['''audio''']
																			__UpperCamelCase  	=      [x['''array'''] for x in speech_samples]
																			return raw_samples

													__UpperCamelCase  	=      type(pt_model.config      )
													if model_config_class in PROCESSOR_MAPPING:
																			__UpperCamelCase  	=      AutoProcessor.from_pretrained(self._local_dir      )
																			if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
																									__UpperCamelCase  	=      processor.tokenizer.eos_token
													elif model_config_class in IMAGE_PROCESSOR_MAPPING:
																			__UpperCamelCase  	=      AutoImageProcessor.from_pretrained(self._local_dir      )
													elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
																			__UpperCamelCase  	=      AutoFeatureExtractor.from_pretrained(self._local_dir      )
													elif model_config_class in TOKENIZER_MAPPING:
																			__UpperCamelCase  	=      AutoTokenizer.from_pretrained(self._local_dir      )
																			if processor.pad_token is None:
																									__UpperCamelCase  	=      processor.eos_token
													else:
																			raise ValueError(F"Unknown data processing type (model config type: {model_config_class})"      )

													__UpperCamelCase  	=      set(inspect.signature(pt_model.forward      ).parameters.keys()      )
													__UpperCamelCase  	=      {}
													if "input_ids" in model_forward_signature:
																			processor_inputs.update(
																			    {
																			        '''text''': ['''Hi there!''', '''I am a batch with more than one row and different input lengths.'''],
																			        '''padding''': True,
																			        '''truncation''': True,
																			    }      )
													if "pixel_values" in model_forward_signature:
																			__UpperCamelCase  	=      load_dataset('''cifar10'''  ,						'''plain_text'''  ,						split='''test'''      )[:2]['''img''']
																			processor_inputs.update({'''images''': sample_images}      )
													if "input_features" in model_forward_signature:
																			__UpperCamelCase  	=      inspect.signature(processor.feature_extractor      ).parameters
																			# Pad to the largest input length by default but take feature extractor default
																			# padding value if it exists e.g. "max_length" and is not False or None
																			if "padding" in feature_extractor_signature:
																									__UpperCamelCase  	=      feature_extractor_signature['''padding'''].default
																									if default_strategy is not False and default_strategy is not None:
																															__UpperCamelCase  	=      default_strategy
																									else:
																															__UpperCamelCase  	=      True
																			else:
																									__UpperCamelCase  	=      True
																			processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': padding_strategy}      )
													if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
																			processor_inputs.update({'''audio''': _get_audio_input(), '''padding''': True}      )
													__UpperCamelCase  	=      processor(**SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      )
													__UpperCamelCase  	=      processor(**SCREAMING_SNAKE_CASE_  ,						return_tensors='''tf'''      )

													# Extra input requirements, in addition to the input modality
													if (
													    config.is_encoder_decoder
													    or (hasattr(SCREAMING_SNAKE_CASE_  ,						'''encoder'''      ) and hasattr(SCREAMING_SNAKE_CASE_  ,						'''decoder'''      ))
													    or "decoder_input_ids" in tf_dummy_inputs
													):
																			__UpperCamelCase  	=      np.asarray([[1], [1]]  ,						dtype=SCREAMING_SNAKE_CASE_      ) * (pt_model.config.decoder_start_token_id or 0)
																			pt_input.update({'''decoder_input_ids''': torch.tensor(SCREAMING_SNAKE_CASE_      )}      )
																			tf_input.update({'''decoder_input_ids''': tf.convert_to_tensor(SCREAMING_SNAKE_CASE_      )}      )

													return pt_input, tf_input



							def      A__		(	self      )-> int:





													'''simple docstring'''






													if version.parse(huggingface_hub.__version__      ) < version.parse('''0.9.0'''      ):
																			raise ImportError(
																			    '''The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'''
																			    ''' installation.'''      )
													else:
																			from huggingface_hub import Repository, create_commit
																			from huggingface_hub._commit_api import CommitOperationAdd

													# Fetch remote data
													__UpperCamelCase  	=      Repository(local_dir=self._local_dir  ,						clone_from=self._model_name      )

													# Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
													__UpperCamelCase  	=      AutoConfig.from_pretrained(self._local_dir      )
													__UpperCamelCase  	=      config.architectures
													if self._override_model_class is not None:
																			if self._override_model_class.startswith('''TF'''      ):
																									__UpperCamelCase  	=      [self._override_model_class[2:]]
																			else:
																									__UpperCamelCase  	=      [self._override_model_class]
																			try:
																									__UpperCamelCase  	=      getattr(import_module('''transformers'''      )  ,						architectures[0]      )
																			except AttributeError:
																									raise ValueError(F"Model class {self._override_model_class} not found in transformers."      )
																			try:
																									__UpperCamelCase  	=      getattr(import_module('''transformers'''      )  ,						'''TF''' + architectures[0]      )
																			except AttributeError:
																									raise ValueError(F"TF model class TF{self._override_model_class} not found in transformers."      )
													elif architectures is None:  # No architecture defined -- use auto classes
																			__UpperCamelCase  	=      getattr(import_module('''transformers'''      )  ,						'''AutoModel'''      )
																			__UpperCamelCase  	=      getattr(import_module('''transformers'''      )  ,						'''TFAutoModel'''      )
																			self._logger.warning('''No detected architecture, using AutoModel/TFAutoModel'''      )
													else:  # Architecture defined -- use it
																			if len(SCREAMING_SNAKE_CASE_      ) > 1:
																									raise ValueError(F"More than one architecture was found, aborting. (architectures = {architectures})"      )
																			self._logger.warning(F"Detected architecture: {architectures[0]}"      )
																			__UpperCamelCase  	=      getattr(import_module('''transformers'''      )  ,						architectures[0]      )
																			try:
																									__UpperCamelCase  	=      getattr(import_module('''transformers'''      )  ,						'''TF''' + architectures[0]      )
																			except AttributeError:
																									raise AttributeError(F"The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."      )

        # Check the TF dummy inputs to see what keys we need in the forward pass
													__UpperCamelCase  	=      tf_class.from_config(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      tf_from_pt_model.dummy_inputs

													del tf_from_pt_model  # Try to keep only one model in memory at a time

													# Load the model and get some basic inputs
													__UpperCamelCase  	=      pt_class.from_pretrained(self._local_dir      )
													pt_model.eval()

													__UpperCamelCase	,				__UpperCamelCase  	=      self.get_inputs(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

													with torch.no_grad():
																			__UpperCamelCase  	=      pt_model(**SCREAMING_SNAKE_CASE_  ,						output_hidden_states=SCREAMING_SNAKE_CASE_      )
													del pt_model  # will no longer be used, and may have a large memory footprint

													__UpperCamelCase  	=      tf_class.from_pretrained(self._local_dir  ,						from_pt=SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      tf_from_pt_model(**SCREAMING_SNAKE_CASE_  ,						output_hidden_states=SCREAMING_SNAKE_CASE_  ,						training=SCREAMING_SNAKE_CASE_      )

													# Confirms that cross loading PT weights into TF worked.
													__UpperCamelCase  	=      self.find_pt_tf_differences(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      {k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
													__UpperCamelCase  	=      {k: v for k, v in crossload_differences.items() if '''hidden''' in k}
													if len(SCREAMING_SNAKE_CASE_      ) == 0 and architectures is not None:
																			raise ValueError(
																			    F"Something went wrong -- the config file has architectures ({architectures}), but no model head"
																			    ''' output was found. All outputs start with \'hidden\''''      )
													__UpperCamelCase  	=      max(output_differences.values()      ) if output_differences else 0.0
													__UpperCamelCase  	=      max(hidden_differences.values()      )
													if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
																			raise ValueError(
																			    '''The cross-loaded TensorFlow model has different outputs, something went wrong!\n'''
																			    + F"\nList of maximum output differences above the threshold ({self._max_error}):\n"
																			    + '''\n'''.join([F"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error]      )
																			    + F"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
																			    + '''\n'''.join([F"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]      )      )

													# Save the weights in a TF format (if needed) and confirms that the results are still good
													__UpperCamelCase  	=      os.path.join(self._local_dir  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      os.path.join(self._local_dir  ,						SCREAMING_SNAKE_CASE_      )
													if (not os.path.exists(SCREAMING_SNAKE_CASE_      ) and not os.path.exists(SCREAMING_SNAKE_CASE_      )) or self._new_weights:
																			tf_from_pt_model.save_pretrained(self._local_dir      )
													del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

													__UpperCamelCase  	=      tf_class.from_pretrained(self._local_dir      )
													__UpperCamelCase  	=      tf_model(**SCREAMING_SNAKE_CASE_  ,						output_hidden_states=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      self.find_pt_tf_differences(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      {k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
													__UpperCamelCase  	=      {k: v for k, v in conversion_differences.items() if '''hidden''' in k}
													if len(SCREAMING_SNAKE_CASE_      ) == 0 and architectures is not None:
																			raise ValueError(
																			    F"Something went wrong -- the config file has architectures ({architectures}), but no model head"
																			    ''' output was found. All outputs start with \'hidden\''''      )
													__UpperCamelCase  	=      max(output_differences.values()      ) if output_differences else 0.0
													__UpperCamelCase  	=      max(hidden_differences.values()      )
													if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
																			raise ValueError(
																			    '''The converted TensorFlow model has different outputs, something went wrong!\n'''
																			    + F"\nList of maximum output differences above the threshold ({self._max_error}):\n"
																			    + '''\n'''.join([F"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error]      )
																			    + F"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
																			    + '''\n'''.join([F"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]      )      )

													__UpperCamelCase  	=      '''Update TF weights''' if self._new_weights else '''Add TF weights'''
													if self._push:
																			repo.git_add(auto_lfs_track=SCREAMING_SNAKE_CASE_      )
																			repo.git_commit(SCREAMING_SNAKE_CASE_      )
																			repo.git_push(blocking=SCREAMING_SNAKE_CASE_      )  # this prints a progress bar with the upload
																			self._logger.warning(F"TF weights pushed into {self._model_name}"      )
													elif not self._no_pr:
																			self._logger.warning('''Uploading the weights into a new PR...'''      )
																			__UpperCamelCase  	=      (
																			    '''Model converted by the [`transformers`\' `pt_to_tf`'''
																			    ''' CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). '''
																			    '''All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n'''
																			    F"Maximum crossload output difference={max_crossload_output_diff:.3e}; "
																			    F"Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"
																			    F"Maximum conversion output difference={max_conversion_output_diff:.3e}; "
																			    F"Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"
																			)
																			if self._max_error > MAX_ERROR:
																									commit_descrition += (
																									    F"\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"
																									)
																			if self._extra_commit_description:
																									commit_descrition += "\n\n" + self._extra_commit_description

																			# sharded model -> adds all related files (index and .h5 shards)
																			if os.path.exists(SCREAMING_SNAKE_CASE_      ):
																									__UpperCamelCase  	=      [
																									    CommitOperationAdd(path_in_repo=SCREAMING_SNAKE_CASE_  ,						path_or_fileobj=SCREAMING_SNAKE_CASE_      )
																									]
																									for shard_path in tf.io.gfile.glob(self._local_dir + '''/tf_model-*.h5'''      ):
																															operations += [
																															    CommitOperationAdd(path_in_repo=os.path.basename(SCREAMING_SNAKE_CASE_      )  ,						path_or_fileobj=SCREAMING_SNAKE_CASE_      )
																															]
																			else:
																									__UpperCamelCase  	=      [CommitOperationAdd(path_in_repo=SCREAMING_SNAKE_CASE_  ,						path_or_fileobj=SCREAMING_SNAKE_CASE_      )]

																			__UpperCamelCase  	=      create_commit(
																			    repo_id=self._model_name  ,						operations=SCREAMING_SNAKE_CASE_  ,						commit_message=SCREAMING_SNAKE_CASE_  ,						commit_description=SCREAMING_SNAKE_CASE_  ,						repo_type='''model'''  ,						create_pr=SCREAMING_SNAKE_CASE_  ,						).pr_url
																			self._logger.warning(F"PR open in {hub_pr_url}"      )






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def 						A_			(				snake_case				: List[str]			,   snake_case				: int			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      pt_attn_layer.in_proj_weight.chunk(3			,   dim=0			)
						__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      pt_attn_layer.in_proj_bias.chunk(3			,   dim=0			)

						__UpperCamelCase  	=      pt_attn_layer.out_proj.weight
						__UpperCamelCase  	=      pt_attn_layer.out_proj.bias

						__UpperCamelCase  	=      q_proj
						__UpperCamelCase  	=      q_proj_bias

						__UpperCamelCase  	=      k_proj
						__UpperCamelCase  	=      k_proj_bias

						__UpperCamelCase  	=      v_proj
						__UpperCamelCase  	=      v_proj_bias

						__UpperCamelCase  	=      out_proj_weights
						__UpperCamelCase  	=      out_proj_bias




def 						A_			(				snake_case				: Optional[Any]			,   snake_case				: int			)   ->     Any:



						'''simple docstring'''

						copy_linear(hf_mlp.fca			,   pt_mlp.c_fc			)
						copy_linear(hf_mlp.fca			,   pt_mlp.c_proj			)




def 						A_			(				snake_case				: Union[str, Any]			,   snake_case				: Optional[Any]			)   ->     Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_linear.weight
						__UpperCamelCase  	=      pt_linear.bias




def 						A_			(				snake_case				: Tuple			,   snake_case				: int			)   ->     str:



						'''simple docstring'''

						copy_linear(hf_layer.layer_norma			,   pt_layer.ln_a			)
						copy_linear(hf_layer.layer_norma			,   pt_layer.ln_a			)

						# copy MLP
						copy_mlp(hf_layer.mlp			,   pt_layer.mlp			)

						# copy attn
						copy_attn_layer(hf_layer.self_attn			,   pt_layer.attn			)




def 						A_			(				snake_case				: List[Any]			,   snake_case				: Any			)   ->     Optional[int]:



						'''simple docstring'''

						for hf_layer, pt_layer in zip(snake_case			,   snake_case			):
												copy_layer(snake_case			,   snake_case			)




def 						A_			(				snake_case				: str			,   snake_case				: Dict			)   ->     List[str]:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_model.token_embedding.weight
						__UpperCamelCase  	=      pt_model.positional_embedding

						# copy layer norm
						copy_linear(hf_encoder.final_layer_norm			,   pt_model.ln_final			)

						# copy hidden layers
						copy_layers(hf_encoder.encoder.layers			,   pt_model.transformer.resblocks			)




def 						A_			(				snake_case				: str			,   snake_case				: List[str]			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model			,   snake_case			)




def 						A_			(				snake_case				: List[str]			,   snake_case				: Any			)   ->     str:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_model.visual.proj.data.T

						# copy layer norms
						copy_linear(hf_model.vision_model.pre_layrnorm			,   pt_model.visual.ln_pre			)
						copy_linear(hf_model.vision_model.post_layernorm			,   pt_model.visual.ln_post			)

						# copy embeds
						__UpperCamelCase  	=      pt_model.visual.conva.weight.data
						__UpperCamelCase  	=      pt_model.visual.class_embedding
						__UpperCamelCase  	=      pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers			,   pt_model.visual.transformer.resblocks			)




@torch.no_grad()
def 						A_			(				snake_case				: List[Any]			,   snake_case				: int			,   snake_case				: Any=None			)   ->     List[Any]:



						'''simple docstring'''

						if config_path is not None:
												__UpperCamelCase  	=      CLIPConfig.from_pretrained(snake_case			)
						else:
												__UpperCamelCase  	=      CLIPConfig(projection_dim=512			,   text_config={}			,   vision_config={}			)

						__UpperCamelCase  	=      CLIPModel(snake_case			).eval()

						__UpperCamelCase	,				__UpperCamelCase  	=      load(snake_case			,   device='''cpu'''			,   jit=snake_case			)
						__UpperCamelCase  	=      pt_model.eval()

						copy_text_model_and_projection(snake_case			,   snake_case			)
						copy_vison_model_and_projection(snake_case			,   snake_case			)
						__UpperCamelCase  	=      pt_model.logit_scale

						__UpperCamelCase  	=      torch.arange(0			,   77			).unsqueeze(0			)
						__UpperCamelCase  	=      torch.randn(1			,   3			,   224			,   224			)

						__UpperCamelCase  	=      hf_model(input_ids=snake_case			,   pixel_values=snake_case			,   return_dict=snake_case			)
						__UpperCamelCase  	=      hf_outputs.logits_per_image
						__UpperCamelCase  	=      hf_outputs.logits_per_text
						__UpperCamelCase	,				__UpperCamelCase  	=      pt_model(snake_case			,   snake_case			)

						assert torch.allclose(snake_case			,   snake_case			,   atol=1e-3			)
						assert torch.allclose(snake_case			,   snake_case			,   atol=1e-3			)

						hf_model.save_pretrained(snake_case			)


if __name__ == "__main__":
				lowercase__				:				Union[str, Any]						=      argparse.ArgumentParser()
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
				parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				lowercase__				:				str						=      parser.parse_args()

				convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




