




"""simple docstring"""




import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification



def 					a__				(       snake_case__       )		->      Any:
			lowerCamelCase       =      []
			embed.append(
			    (
			        F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
			        F'stage{idx}.patch_embed.proj.weight',
			    )       )
			embed.append(
			    (
			        F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
			        F'stage{idx}.patch_embed.proj.bias',
			    )       )
			embed.append(
			    (
			        F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
			        F'stage{idx}.patch_embed.norm.weight',
			    )       )
			embed.append(
			    (
			        F'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
			        F'stage{idx}.patch_embed.norm.bias',
			    )       )
			return embed



def 					a__				(       snake_case__ ,				snake_case__       )		->      Union[str, Any]:
			lowerCamelCase       =      []
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
			        F'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
			        F'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
			        F'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
			        F'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
			        F'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
			        F'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
			        F'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
			        F'stage{idx}.blocks.{cnt}.attn.proj.weight',
			    )       )
			attention_weights.append(
			    (
			        F'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
			        F'stage{idx}.blocks.{cnt}.attn.proj.bias',
			    )       )
			attention_weights.append(
			    (F'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', F'stage{idx}.blocks.{cnt}.mlp.fc1.weight')       )
			attention_weights.append(
			    (F'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', F'stage{idx}.blocks.{cnt}.mlp.fc1.bias')       )
			attention_weights.append(
			    (F'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', F'stage{idx}.blocks.{cnt}.mlp.fc2.weight')       )
			attention_weights.append(
			    (F'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', F'stage{idx}.blocks.{cnt}.mlp.fc2.bias')       )
			attention_weights.append(
			    (F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', F'stage{idx}.blocks.{cnt}.norm1.weight')       )
			attention_weights.append(
			    (F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', F'stage{idx}.blocks.{cnt}.norm1.bias')       )
			attention_weights.append(
			    (F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', F'stage{idx}.blocks.{cnt}.norm2.weight')       )
			attention_weights.append(
			    (F'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', F'stage{idx}.blocks.{cnt}.norm2.bias')       )
			return attention_weights



def 					a__				(       snake_case__       )		->      Union[str, Any]:
			lowerCamelCase       =      []
			token.append((F'cvt.encoder.stages.{idx}.cls_token', """stage2.cls_token""")       )
			return token



def 					a__				(       )		->      Union[str, Any]:
			lowerCamelCase       =      []
			head.append(("""layernorm.weight""", """norm.weight""")       )
			head.append(("""layernorm.bias""", """norm.bias""")       )
			head.append(("""classifier.weight""", """head.weight""")       )
			head.append(("""classifier.bias""", """head.bias""")       )
			return head



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )		->      str:
			lowerCamelCase       =      'imagenet-1k-id2label.json'
			lowerCamelCase       =      10_00

			lowerCamelCase       =      'huggingface/label-files'
			lowerCamelCase       =      num_labels
			lowerCamelCase       =      json.load(open(cached_download(hf_hub_url(__lowercase ,				__lowercase ,				repo_type="""dataset"""       )       ) ,				"""r"""       )       )
			lowerCamelCase       =      {int(__lowercase       ): v for k, v in idalabel.items()}

			lowerCamelCase       =      idalabel
			lowerCamelCase       =      {v: k for k, v in idalabel.items()}

			lowerCamelCase       =      CvtConfig(num_labels=__lowercase ,				idalabel=__lowercase ,				labelaid=__lowercase       )

			# For depth size 13 (13 = 1+2+10)
			if cvt_model.rsplit("""/""" ,				1       )[-1][4:6] == "13":
						lowerCamelCase       =      [1, 2, 10]

			# For depth size 21 (21 = 1+4+16)
			elif cvt_model.rsplit("""/""" ,				1       )[-1][4:6] == "21":
						lowerCamelCase       =      [1, 4, 16]

			# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
			else:
						lowerCamelCase       =      [2, 2, 20]
						lowerCamelCase       =      [3, 12, 16]
						lowerCamelCase       =      [1_92, 7_68, 10_24]

			lowerCamelCase       =      CvtForImageClassification(__lowercase       )
			lowerCamelCase       =      AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""       )
			lowerCamelCase       =      image_size
			lowerCamelCase       =      torch.load(__lowercase ,				map_location=torch.device("""cpu"""       )       )

			lowerCamelCase       =      OrderedDict()
			lowerCamelCase       =      []

			for idx in range(len(config.depth       )       ):
						if config.cls_token[idx]:
									lowerCamelCase       =      list_of_state_dict + cls_token(__lowercase       )
						lowerCamelCase       =      list_of_state_dict + embeddings(__lowercase       )
						for cnt in range(config.depth[idx]       ):
									lowerCamelCase       =      list_of_state_dict + attention(__lowercase ,				__lowercase       )

			lowerCamelCase       =      list_of_state_dict + final()
			for gg in list_of_state_dict:
						print(__lowercase       )
			for i in range(len(__lowercase       )       ):
						lowerCamelCase       =      original_weights[list_of_state_dict[i][1]]

			model.load_state_dict(__lowercase       )
			model.save_pretrained(__lowercase       )
			image_processor.save_pretrained(__lowercase       )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
					__A							:    List[str]  							=							argparse.ArgumentParser()
					parser.add_argument(
					    """--cvt_model""",
					    default="""cvt-w24""",
					    type=str,
					    help="""Name of the cvt model you'd like to convert.""",
					)
					parser.add_argument(
					    """--image_size""",
					    default=384,
					    type=int,
					    help="""Input Image Size""",
					)
					parser.add_argument(
					    """--cvt_file_name""",
					    default=r"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
					    type=str,
					    help="""Input Image Size""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
					)

					__A							:    Union[str, Any]  							=							parser.parse_args()
					convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)









"""simple docstring"""




import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
					import tensorflow as tf

if is_torch_available():
					import torch


lowerCAmelCase							:    List[str]  							=							logging.get_logger(__name__)






class 					__magic_name__							:






				'''simple docstring'''





				def __init__( self	,				_a = None	,				_a = None	,				_a=None	,				_a=None				):





							"""simple docstring"""
							if not conversation_id:
										lowerCamelCase       =      uuid.uuida()
							if past_user_inputs is None:
										lowerCamelCase       =      []
							if generated_responses is None:
										lowerCamelCase       =      []

							lowerCamelCase       =      conversation_id
							lowerCamelCase       =      past_user_inputs
							lowerCamelCase       =      generated_responses
							lowerCamelCase       =      text





				def __eq__( self	,				_a				):





							"""simple docstring"""
							if not isinstance(_a	,				_a				):
										return False
							if self.uuid == other.uuid:
										return True
							return (
							    self.new_user_input == other.new_user_input
							    and self.past_user_inputs == other.past_user_inputs
							    and self.generated_responses == other.generated_responses
							)





				def      _lowerCAmelCase       ( self	,				_a	,				_a = False				):





							"""simple docstring"""
							if self.new_user_input:
										if overwrite:
													logger.warning(
													    f'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
													    f'with: "{text}".'				)
													lowerCamelCase       =      text
										else:
													logger.warning(
													    f'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
													    f'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'				)
							else:
										lowerCamelCase       =      text





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							if self.new_user_input:
										self.past_user_inputs.append(self.new_user_input				)
							lowerCamelCase       =      None





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							self.generated_responses.append(_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							for user_input, generated_response in zip(self.past_user_inputs	,				self.generated_responses				):
										yield True, user_input
										yield False, generated_response
							if self.new_user_input:
										yield True, self.new_user_input





				def __repr__( self				):





							"""simple docstring"""
							lowerCamelCase       =      f'Conversation id: {self.uuid} \n'
							for is_user, text in self.iter_texts():
										lowerCamelCase       =      """user""" if is_user else """bot"""
										output += f'{name} >> {text} \n'
							return output






@add_end_docstrings(
    UpperCAmelCase__		,      R"\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    "		,      )
class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''





				def __init__( self	,				*_a	,				**_a				):





							"""simple docstring"""
							super().__init__(*_a	,				**_a				)
							if self.tokenizer.pad_token_id is None:
										lowerCamelCase       =      self.tokenizer.eos_token





				def      _lowerCAmelCase       ( self	,				_a=None	,				_a=None	,				_a=None	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      {}
							lowerCamelCase       =      {}
							lowerCamelCase       =      {}

							if min_length_for_response is not None:
										lowerCamelCase       =      min_length_for_response
							if minimum_tokens is not None:
										lowerCamelCase       =      minimum_tokens

							if "max_length" in generate_kwargs:
										lowerCamelCase       =      generate_kwargs["""max_length"""]
										# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
							if clean_up_tokenization_spaces is not None:
										lowerCamelCase       =      clean_up_tokenization_spaces

							if generate_kwargs:
										forward_params.update(_a				)
							return preprocess_params, forward_params, postprocess_params





				def __call__( self	,				_a	,				_a=0	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      super().__call__(_a	,				num_workers=_a	,				**_a				)
							if isinstance(_a	,				_a				) and len(_a				) == 1:
										return outputs[0]
							return outputs





				def      _lowerCAmelCase       ( self	,				_a	,				_a=32				):





							"""simple docstring"""
							if not isinstance(_a	,				_a				):
										raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""				)
							if conversation.new_user_input is None:
										raise ValueError(
										    f'Conversation with UUID {type(conversation.uuid				)} does not contain new user input to process. '
										    """Add user inputs with the conversation's `add_user_input` method"""				)
							if hasattr(self.tokenizer	,				"""_build_conversation_input_ids"""				):
										lowerCamelCase       =      self.tokenizer._build_conversation_input_ids(_a				)
							else:
										# If the tokenizer cannot handle conversations, we default to only the old version
										lowerCamelCase       =      self._legacy_parse_and_tokenize(_a				)

							if self.framework == "pt":
										lowerCamelCase       =      torch.LongTensor([input_ids]				)
							elif self.framework == "tf":
										lowerCamelCase       =      tf.constant([input_ids]				)
							return {"input_ids": input_ids, "conversation": conversation}





				def      _lowerCAmelCase       ( self	,				_a	,				_a=10	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      generate_kwargs.get("""max_length"""	,				self.model.config.max_length				)

							lowerCamelCase       =      model_inputs["""input_ids"""].shape[1]
							if max_length - minimum_tokens < n:
										logger.warning(f'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'				)
										lowerCamelCase       =      max_length - minimum_tokens
										lowerCamelCase       =      model_inputs["""input_ids"""][:, -trim:]
										if "attention_mask" in model_inputs:
													lowerCamelCase       =      model_inputs["""attention_mask"""][:, -trim:]
							lowerCamelCase       =      model_inputs.pop("""conversation"""				)
							lowerCamelCase       =      max_length
							lowerCamelCase       =      self.model.generate(**_a	,				**_a				)
							if self.model.config.is_encoder_decoder:
										lowerCamelCase       =      1
							else:
										lowerCamelCase       =      n
							return {"output_ids": output_ids[:, start_position:], "conversation": conversation}





				def      _lowerCAmelCase       ( self	,				_a	,				_a=True				):





							"""simple docstring"""
							lowerCamelCase       =      model_outputs["""output_ids"""]
							lowerCamelCase       =      self.tokenizer.decode(
							    output_ids[0]	,				skip_special_tokens=_a	,				clean_up_tokenization_spaces=_a	,				)
							lowerCamelCase       =      model_outputs["""conversation"""]
							conversation.mark_processed()
							conversation.append_response(_a				)
							return conversation





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      self.tokenizer.eos_token_id
							lowerCamelCase       =      []
							for is_user, text in conversation.iter_texts():
										if eos_token_id is not None:
													input_ids.extend(self.tokenizer.encode(_a	,				add_special_tokens=_a				) + [eos_token_id]				)
										else:
													input_ids.extend(self.tokenizer.encode(_a	,				add_special_tokens=_a				)				)

							if len(_a				) > self.tokenizer.model_max_length:
										lowerCamelCase       =      input_ids[-self.tokenizer.model_max_length :]
							return input_ids



